knowledge economy research articles

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The Knowledge Economy

We define the knowledge economy as production and services based on knowledge-intensive activities that contribute to an accelerated pace of technical and scientific advance, as well as rapid obsolescence. The key component of a knowledge economy is a greater reliance on intellectual capabilities than on physical inputs or natural resources. We provide evidence drawn from patent data to document an upsurge in knowledge production and show that this expansion is driven by the emergence of new industries. We then review the contentious literature that assesses whether recent technological advances have raised productivity. We examine the debate over whether new forms of work that embody technological change have generated more worker autonomy or greater managerial control. Finally, we assess the distributional consequences of a knowledge-based economy with respect to growing inequality in wages and high-quality jobs.

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Organizations in the knowledge economy. An investigation of knowledge-intensive work practices across 28 European countries

Journal of Advances in Management Research

ISSN : 0972-7981

Article publication date: 10 May 2022

Issue publication date: 24 January 2023

This paper aims to investigate whether the shift towards the knowledge economy (e.g. an increasing reliance in knowledge in the production of goods and services) is related to the work practices of organizations (aimed at the provision of autonomy, investments in training and the use of technology).

Design/methodology/approach

The analyses are based on data about over 20,000 companies in 28 European countries. National level indicators of knowledge intensity are related to the work practices of these organizations. Multilevel analysis is applied to test hypotheses.

The results show that there is a strong and positive relationship between the knowledge intensity of the economy and the use of knowledge intense work practices.

Originality/value

To the best of our knowledge, this is one of the first papers to test whether knowledge intensity at the national level is related to the work practices of organizations.

Organizational design
Knowledge management
Diffusion of innovation
Knowledge economy

Koster, F. (2023), "Organizations in the knowledge economy. An investigation of knowledge-intensive work practices across 28 European countries", Journal of Advances in Management Research , Vol. 20 No. 1, pp. 140-159. https://doi.org/10.1108/JAMR-05-2021-0176

Emerald Publishing Limited

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

The shift toward the knowledge economy is believed to move organizations toward more flexibility, decentralization and autonomy ( Alvesson, 2004 ; Powell and Snellman, 2004 ; Foss, 2005 ; Makani and Marche, 2010 ; Khadir-Poggi and Keating, 2013 ; Lee, 2015 ; Ullah and Narain, 2020 ; Valeri, 2021 ). A basic assumption in these studies is that structures and practices of organizations are affected by the environment in which they operate ( Neef, 1999 ; Ferreira et al ., 2018 ). While prior studies generated important insights, there are several ways to improve our understanding of the link between the knowledge economy and organizational practices. The present article aims to close three gaps in the current literature.

First, much of the research provides indirect evidence by focusing on the connection between the knowledge economy and work, rather than on the practices of organizations. For example, previous studies investigating how factors such as globalization and technological change affect labor demand ( Wood, 1987 ; Burris, 1998 ; Cerny, 1999 ; Martinaitis et al ., 2020 ) focus on tasks and jobs of workers ( Aoyama and Castells, 2002 ; Fernández-Macías, 2012 ; Sakamoto et al ., 2018 ; Kalleberg and Mouw, 2018 ). Other studies do center on organizations and the role of decentralization, adaptation and innovation in knowledge intense economies ( Switzer, 2008 ; Hodgson, 2016 ), but they remain largely theoretical. The present study fills this void by analyzing three knowledge-intensive work practices (KIWPs), namely (1) decentralized decision making (producing knowledge), (2) organizational learning practices (developing and distributing knowledge) and (3) the use of technology (applying knowledge). These work practices are derived from three approaches to organizations, namely the dynamic capabilities approach, organizational learning theories and models of technology adoption and diffusion.

Secondly, it is necessary to think about the knowledge economy in the sense of the degree of knowledge intensity of the economy, instead of stating that modern organizations operate in a knowledge economy ( Powell and Snellman, 2004 ; Manville and Ober, 2003 ; Nurunnabi, 2017 ). In this study, innovation is the independent rather than the dependent variable, which is common in innovation research (e.g. Crossan and Apaydin, 2010 ; Koster, 2021 ). In the present study, innovations serve as the starting point and the question is whether this creates an environment to which organizations adapt. Hence, the analyses relate to organizational mechanisms, such as learning and dynamic capabilities, emphasizing how organizations change their structures and processes to remain competitive ( Felin and Powell, 2016 ; Kapoor and Aggarwal, 2020 ). This leads to a more fine-grained understanding of how the knowledge economy affects organizations.

Finally, investigating how knowledge intensification of the economy relates to the work practices of organizations requires comparative research of organizations operating in different environments. Since knowledge intensification of the economy is a macro level factor, cross national research is required. Therefore, empirical research is conducted connecting the economy and the practices of organizations operating in different countries . Hence, this study answers to the call in the literature for more organizational research across countries ( Greenwood et al ., 2014 ).

To date, these three conditions are not met in a single study. Instead, there are studies of knowledge-intensive organizations and about the knowledge economy. But there is very little connection between them, while this is needed to show how the knowledge economy relates to the management of organizations. Gaining insights in the relationship between knowledge intensification of the economy and the work practices of organizations also has practical value for managers and consultants. Most and for all, this research helps organizations identifying which practices matter if they aim to increase their knowledge intensity. Furthermore, considering that economies vary in their level of knowledge intensity, research showing that knowledge intensification relates to organizational practices enables learning across organizations. For organizations in less knowledge intense economies, this information can help them to prepare them for the future (considering that economies move in the direction of increased knowledge intensity) and for organizations in the more knowledge intense economies it provides a benchmark to assess whether their current practices fit their environment.

Data of 20,672 companies in 28 countries gathered through the fourth European Company Survey (ECS), which was held in 2019, are combined with country level data about the knowledge intensity of the economy. These national level data (the European Innovation Scoreboard: EIS) are constructed by the European Commission (2020) . This article is structured as follows. In the next section, the link between knowledge intensity of the economy and knowledge intense work practices is further theorized and explored. The theoretical section results in the general hypothesis that organizations use more knowledge intense work practices if they operate in a knowledge economy, which is then tested using a multilevel regression analysis. Theoretical and practical implications are discussed in the closing section.

Work practices in the knowledge economy

The term knowledge economy is often used, but not always clearly defined. Powell and Snellman (2004) are an exception as their overview of studies into the knowledge economy starts with a clear definition, namely: the production of services and goods based on “knowledge-intensive activities” and a “greater reliance on intellectual capabilities” (p. 201). Technological changes and innovations underlie this shift in production processes. Although it is generally accepted that economies tend to move in the direction of a knowledge economy, there is some debate about the way in which it proceeds. The two sides of this debate focus on different aspects of the relationship between technology and economy. There are those believing that economies are entering a completely new phase that can be termed a “new economy”. These authors tend to argue that there is an abrupt break with the old economy because of technological change (see for example Schwab, 2016 ). Such conceptualizations of the economy view it as a series of distinguishable periods. However, historically it has happened too often that such breaks were announced, while afterward they turned out to be gradual changes and much of the economy stayed the same. This change-within-stability position is therefore taken by many researchers ( Boyd and Holton, 2018 ). A different position is taken by those arguing that one should look at the outcomes of these changes to conclude whether economies are entering a new era. This position is for example reflected by the well-known “productivity paradox” as well as by the theoretical argument that technological change is far less deterministic than is often assumed ( Burris, 1998 ; Swedberg, 2005 ; Boyd and Holton, 2018 ; Brynjolfsson et al ., 2018 ).

In other words, several researchers point out that technological change is not automatically visible in organizations, work practices, or productivity. Nevertheless, that technological change takes place is not disputed. In other words, while we are not entering a completely new economic reality disrupting the old economy, it is acknowledged that changes occur. For these changes to be visible, technologies need to be applied and used by workers and organizations to be effective ( Foster and Rosenzweig, 2010 ; Wensing and Krol, 2019 ). Conceptualizing the knowledge economy in terms of varying levels of knowledge intensity rather than dichotomously (stating that an economy is either a knowledge economy or not) is more accurate. It is a matter of degree.

The more knowledge intense the economy, the stronger the emphasis is on the producing, diffusing and applying knowledge ( Adler, 2001 ; Powell and Snellman, 2004 ). These knowledge-intensive activities are sometimes termed “knowledge work”. Nevertheless, the term needs further clarification as it refers to different aspects. Different conceptualizations can be found across the literature ( Kelloway and Barling, 2000 ). A large share of research regards knowledge work as work that is performed by a specific group and focuses on professions and occupations (e.g. Adler et al ., 2008 ; Paton, 2013 ). Other researchers emphasize the importance of personal characteristics and activities, such as creativity and individual learning, that enable workers to thrive in the knowledge economy. And still others argue that knowledge works implies specific behaviors like creating and sharing knowledge and information ( Kelloway and Barling, 2000 ). Without a doubt, occupations, persons, activities and organizational behavior are important aspects of knowledge work. Nevertheless, it overlooks the organizational side of the knowledge economy. That is, it does not include the mechanisms that are necessary for an organization to function in a knowledge-intensive environment. At the same time, it is through these organizational mechanisms that the knowledge economy affects the content of work, which then in turn leads to changes in the occupational structure and demand different kinds of activities and behavior in the workplace. The literature on knowledge-intensive organizations provides insights in some of the mechanisms and work practices that are central to such organizations stimulating and enabling the production, diffusion and use of knowledge ( Pyöriä, 2005 ).

Due to technological changes, the economic landscape reflects a system of economic opportunities based on connections and information ( Carlsson, 2004 ). This economic landscape is characterized by increasing knowledge intensity, a stronger focus on the adaptability of organizations as they operate in an economic system that is more dynamic due to the use of technology and the need to process information inside and outside organizations. The next question is what the implications are for the organization of work.

While there is some debate how to analyze knowledge work (and whether it refers to work in specific sectors or occupation or should be viewed as a kind of work behavior), when focusing on the characteristics of this work, the following stand out. Pyöriä (2005) distinguishes two ideal types of work, namely “traditional” and “knowledge” work. Knowledge work (1) puts more emphasis on professionalism, self-management and job and task circulation compared to traditional work; (2) is less standardized than traditional work; and (3) requires more learning (both in terms of formal education and on-the-job learning) compared to traditional work. Instead of focusing on the extremes of these types, it is also possible to interpret them as relative positions on a continuum from traditional to knowledge work. Again, it is not an either/or situations, but a matter of degree whether an organization has these knowledge work characteristics. In the next section, it is hypothesized how organizational practices relate to knowledge intensification.

Explaining KIWPs

Several organizational theories explain why organizations would apply certain practices in more knowledge intense economies. The main explanations are provided by theories concerning dynamic capabilities, organizational learning and the use of technology. Each of these set of theories focuses on different aspects of knowledge processes that are central to the knowledge economy.

From this it follows that it is expected that the more knowledge intense the economic environment is, the more organizations rely on decentralization.

From this it follows that it is expected that the more knowledge intense the economic environment is, the more organizations rely on formal and informal learning.

From this it follows that it is expected that the more knowledge intense the economic environment is, the more organizations have technology-related work practices.

Hence, the overall prediction is that it is expected that the more knowledge intense the economic environment is, the more organizations have KIWPs.

Figure 1 presents the research model that was developed in the previous sections. On top, there are the national level factors that are hypothesized to be related to the knowledge-intensive work practices that reside at the organizational level. Besides that, the figure acknowledges that knowledge intensity is not the only explanation of KIWPs and that there may be other factors at the organizational and the national level that also explain them.

Data and methods

Data from different sources are combined to conduct the analyses. Data at the organizational level are available through the ECS 2019 (ECS; Van Houten and Russo, 2020 ). The data for this survey are collected by the European Foundation for the Improvement of Living and Working Conditions (Eurofound) and the European Center for the Development of Vocational Training (Cedefop). This fourth edition of the ECS contains information about 21,869 establishments in 28 European countries (27 EU (European Union) member states and the UK). These company level data are combined with data at the national level. These national level data provide the possibility to investigate the impact of the knowledge intensity of the economy on KIWPs. Besides that, several control variables at the national level are included in the analyses. These national level data are accessed through Eurostat. The national level data are available for all 28 countries. There are, however, some missing values in the ECS; a total of 1,197 companies did not provide responses to the dependent variable in this study. These companies are excluded, and the analyses are carried out on the remaining 20,672 companies.

Knowledge-intensive work practices (KIWPs)

For how many employees in this establishment does their job include finding solutions to unfamiliar problems they are confronted with? Your best estimate is good enough. [ Unfamiliair problems ].

For how many employees in this establishment do their job include independently organizing their own time and scheduling their own tasks? Your best estimate is good enough. [ Scheduling ].

How many employees in this establishment are in jobs that require continuous training? Your best estimate is good enough. [ Continuous training ].

In 2018, how many employees in this establishment participated in training sessions on the establishment premises or at other locations during paid working time? Your best estimate is good enough. [ Participation in training ].

In 2018, how many employees in this establishment have received on-the-job training or other forms of direct instruction in the workplace from more experienced colleagues? Your best estimate is good enough. [ On-the-job training ].

How many employees in this establishment use personal computers or laptops to carry out their daily tasks? Your best estimate is good enough. [ Computers ].

Respondent are asked to indicate on a seven-point scale for each of these questions to how many of the employees it applies. The categories of these items are as follows: none at all, less than 20%, 20–39%, 40–59%, 60–79%, 80–99% and all.

Since these are separate items in the questionnaire, there is the possibility that they measure aspects of organizations that are unrelated. To assess whether the items measure a construct that can be labeled KIWPs, a reliability analysis is performed. This analysis shows that the items have a Cronbach's alpha of 0.71. Therefore, it is concluded that the items can be combined into a single scale. This scale is created by adding the scores on the items. These scores are divided by six. Hence the final scale also runs from 1 to 7.

Knowledge intensity economy

a. Human resources (educational level)

b. Attractive research systems (international competitiveness)

c. Innovation-friendly environment (opportunities)

a. Finance and support (financial resources)

b. Firm investments (knowledge investments)

a. Innovators (number of innovators)

b. Linkages (collaboration)

c. Intellectual assets (property rights)

a. Employments impacts (knowledge intense employment)

b. Sales impacts (exports and sales)

For detailed information about the dimensions and the underlying indicators, see the EIS 2020 methodology report ( European Commission, 2020 ). The indicators are measured with Eurostat data. Following an extensive procedure, these data are transformed to calculate an index: The Innovation Index that runs from 0–1 in theory. The Innovation Index is used as an indicator for measuring the knowledge economy. Powell and Snellman (2004 , p. 202) mention different approaches to measuring the knowledge economy, such as a focus on “stock of knowledge” (e.g. human, organizational and intellectual capital) or “activities” (e.g. R&D efforts, investments in information and communication technology). The advantage of the Innovation Index is that it does not have a narrow focus and integrates these approaches into a single measure. Hence, it includes several dimensions that are generally assumed to be central to the knowledge economy.

Control variables

At the national level, the analyses of knowledge-intensive work practices are controlled for GDP per capita in purchasing power standards (GDP pps) and the level of unemployment. Both statistics are taken from the Eurostat database. GDP pps expressed in relative terms, with a score of 100 as the EU average. The variable has a minimum of 53 and a maximum of 260 (the values are divided by 10). The level of unemployment is indicated with the percentage of the labor force without work and lies between 2 and 19%.

At the level of the establishments, several control variables are included. Three variables measure the use of technology by these establishments by asking whether they use it or not. Hence, these variables are dummy variables, with a 0 indicating that these technologies are not present and a 1 that they are. The variable robots are measured with the question “Robots are programmable machines that are capable of carrying out a complex series of actions automatically, which may include the interaction with people. Does this establishment use robots?”, the variable IT production is measured with the question “Does this establishment use data analytics (Data analytics refers to the use of digital tools for analysing data collected at this establishment or from other sources) to improve the processes of production or service delivery, and the variable IT monitoring is measured with the question “Does this establishment use data analytics to monitor employee performance?”. The variable hierarchical levels is measured with the question “… how many hierarchical levels do you have in this establishment?”. Then there are three questions measured on a 7-point scale (none at all, less than 20%, 20–39%, 40–59%, 60–79%, 80–99%, and all) about the employees in the establishment. The variable % managers is measured with the question “How many people that work in this establishment are managers? Your best estimate is good enough”, the variable % permanent employees is measured with the question “How many employees in this establishment have an open-ended contract? Your best estimate is good enough”, and the variable % part-time is measured with the question “How many employees in this establishment work part-time (part time refers to working less than 35 h per week)? Your best estimate is good enough”. The final variable organizational size is measured by asking the question “Approximately how many people work in this establishment?”. The variable organizational size has three categories, namely: 10–49, 50–249 and 250 and more.

Table 1 provides the summary statistics for all variables included in this study. As this table shows, the most common of the six KIWPs relates to the use of PCs and laptops and on-the-job-training. Secondly, there is considerable variation in the measure of knowledge intensity of the economy. Robots are used in 12% of the companies and IT systems for production and monitoring are applied more often (49 and 32% respectively). Finally, it is worth noting that organizations of different sizes and from all economic sectors are represented. Regarding organizational size, Table 1 shows that most organizations are small (with 10–49 workers).

The dataset has a hierarchical structure: companies are nested in countries. The overall expectation is that the knowledge economy at the country level is related to the knowledge intensity of the work practices that these companies apply. To get an accurate estimation it is necessary to take account of this nested structure of the data. Therefore, multilevel models ( Snijders and Bosker, 2011 ; Bickel, 2007 ) are estimated. Such models enable to distinguish variance at different levels, in this case the country level and the company level. To assess these models, several statistics are computed. The intraclass correlation coefficients (ICC) indicate how much of the variance resides at these two levels. The ICC provides information about the extent to which there is country level variance that can be explained by adding variables at that level. The next step in multilevel modeling is to investigate what happens to these variances after adding these variables. By adding company and country level variables, the ICC becomes smaller if they explain national level variation in KIWPs. Furthermore, by focusing on the variances at the two levels and by examining how they change after the additional variables are included, it is possible to assess how much of that variance is explained at the country level as well as at the company level. The fit of the multilevel models is investigated with likelihood ratio tests ( Peugh, 2010 ; Snijders and Bosker, 2011 ; Hox et al ., 2017 ). This test does not provide enough information to assess the fit of the model. However, by comparing the likelihood tests between the models, allows to test whether the difference between the models is statistically significant. The difference is called the deviance and is tested with a chi-squared test (with the number of parameters that are tested as the degrees of freedom) ( Snijders and Bosker, 2011 ).

For the present analyses, several steps are taken to test the hypotheses. First, the focus is on the composite measure of KIWPs (based on the results of the scale analysis). To get a baseline against which the consecutive models are compares, a so-called empty model is estimated. This empty model does not include explanatory variables and provides that variances at the country and the company level. Then a model is estimated with the control variables at the country level and the company level. The third model also includes the indicator for the knowledge economy (the Innovation Index). Besides testing the significance of these explanatory variables, it is also investigated whether it leads to an improvement of the model if these variables are added. Furthermore, it is assessed how much of the variance at the two levels is explained. Next to that, several other models are estimated, namely models with the separate KIWPs (six in total) and models in which the sub-dimensions of the Innovation Index are investigated separately. The models are structured using the same procedure as the models with the composite measure as the dependent variable. These additional tests serve as sensitivity analyses to see how robust the initial models are if other specifications are chosen.

Based on the country averages of the KIWPs (presented in Table 2 ), it can be concluded that the use of these practices is more common in Sweden, the UK and Finland and that companies in Romania, Bulgaria and Lithuania make less use of these practices. On a scale from 1–7, the average use in Sweden is 4.6 and 2.6 in Romania. Focusing on their scores on the Innovation Index, Sweden and Romania also have the highest and the lowest scores (0.71 and 0.16, respectively). To get a first impression of how the knowledge economy relates to the use of KIWPs, the correlation between the Innovation Index and the country level mean of the use of KIWPs is calculated. This analysis shows that there is a positive and statistically significant associated between them ( r = 0.78). Figure 2 shows this relationship. Clearly, there is a positive association between the innovation index and the use of KIWPs at the aggregate level. The next question is whether this association also holds in a multilevel framework controlling for factors at the national level and the level of the companies. To investigate this, several multilevel regression models are calculated.

Table 3 presents the results of the multilevel analysis with the composite measure of KIWPs as the dependent variable. Starting with the control variables, the following insights are gained. At the national level, GDP pps is positively related to the use of KIWPs. Furthermore, several characteristics at the company level explain the use of KIWPs. Notably, companies relying on robots and the two kinds of data analytics positively relate to the use of KIWPs. These practices are also more often applied in companies with more managers, more permanent workers, more fulltime workers and in smaller companies. Finally, it turns out that there are sectoral differences. The results show that KIWPs are particularly used by companies in three sectors: financial and insurance activities, professional, scientific and technical activities, and information and communication. Overall, these results emphasize the role of knowledge intensity at the organizational level in explaining KIWPs.

The final model presented in Table 3 also includes the knowledge economy indicator (the Innovation Index). Regarding the control variables, adding this indicator does not change the results for the company level variables, whereas the effect of GDP disappears. The latter finding suggests that higher GDP results in more knowledge intensity of the economy. Furthermore, there is a significant and positive relationship between the Innovation Index at the national level and the use of KIWPs, thus supporting the overall hypothesis that the knowledge economy is positively related with the knowledge intense work practices of organizations.

Looking at the explanatory contribution of the different models, the following is concluded. First, based on the empty model (the model without explanatory variables), Table 3 shows that 12% of the variance in KIWPs can be attributed to the national level. The other 88% is due to variance at the company level. Secondly, adding the control variables at the company and the country level reduces the variance at both levels. At the company level the reduction is 23% and at the country level the reduction is 31%. Finally, in the third model shown in Table 3 which includes the knowledge economy, the share of country level variances drops to 3% (implying a change of 75% in the intraclass correlation coefficient). Then, comparing the variance at the national level in the final model with that of the empty model, the Innovation Index reduces it with 80%. Put differently, besides having a positive and statistically significant impact, the Innovation Index explains a large share of the country level variance in KIWPs.

Before stating the overall hypothesis ( Hypothesis 4 ), three sub-hypotheses about the knowledge economy were formulated, namely that the knowledge economy would be related to decentralization, (formal and informal) learning and the use of technology. The models presented in Tables 4 and 5 , test these hypotheses, using information about unfamiliar problems and scheduling to indicate decentralization, variables measuring continuous learning, participation in training and on-the-job training as indicators for training, and the use of computers to indicator technology-use. The results of these models are in line with the three separate hypotheses: The Innovation Index is positively related with each of these indicators. Hence it is concluded that Hypothesis 1 – 3 also find empirical support.

Regarding the extent to which these indicators are explained by the knowledge economy, it turns out that the reduction in country level variance is the strongest in the use of work practices related to decentralization (71 and 53% for scheduling and unfamiliar problems, respectively) and that the reduction in continuous learning is the lowest (21%). All in all, the empirical results are in line with the four research hypotheses formulated in the theoretical section.

Finally, models are estimated with the four sub-dimensions of the Innovation Index (framework conditions, investments, innovation activities and impacts), as the explanatory variables (these models are not reported to save space, but they are available upon request). These variables are investigated separately. The analyses show that each of these dimensions positively relates to KIWPs, with the indicator measuring framework conditions, which include human resources, attractiveness of the research system and innovation-friendliness of the environment, leading to the largest reduction of variance at the country level (compared to the empty model, the country level variance is reduced with 75%) and impacts, in terms of employment and sales, having the lowest reduction in country level variance (a reduction of 56%). Hence, while there is some variation in the impact across the different indicators of KIWPs on the one hand and the sub-dimensions of the Innovation Index, these additional analyses indicate the outcomes remain if the focus is on these separate indicators. It should also be noted that the strongest results if composite indicators are used: an overall indicator of the knowledge economy with a summary score for KIWPs leads to the strongest (in terms of explained variance) models.

Discussion and conclusion

This paper focuses on how the knowledge management practices of organizations relate to the structure of the economy in which they operate. The analysis of a representative sample of companies in 28 European countries leads to the overall conclusion that the more knowledge intense an economy is, the more likely it is that organizations apply knowledge intense work practices. This insight has several implications. First, it shows that developments external to organizations has consequences for the way they are structured, in this case: that the economic conditions that organizations deal with are transferred into the way they manage their knowledge. Whereas this is often assumed, researching organizational structures in a cross-national comparative perspective is not a common practice in organizational studies. Hence, empirically, this link is not often established. Secondly, the implication of this finding is that the knowledge intensity of the economy is associated with the knowledge intensity of the work practices for all organizations in an economy. This in turn provides evidence for the discussion about how to understand the knowledge intensification of the economy and whether this concentrated in certain sector of the economy or whether it means that it affects all organizations. Based on the empirical results of this paper, the conclusion is in favor of the latter; the knowledge economy impacts all organizations.

Regarding the application of knowledge-intensive work practices, the following is concluded. Most and for all, the analyses show that organizations apply these practices in unison rather than as separate and isolated means of governing knowledge. This finding was mainly corroborated with the empirical result that these practices are positively related with each other and form a sufficiently reliable scale. Besides that, the finding that the knowledge intensity of the economy equally affects the underlying indicators, further strengthens the idea that organizations apply these practices as a bundle rather than focusing on each of these practices individually. This, then has implication for theories concerning the knowledge management of organizations. As was explained in the theory section, there are at least three theoretical approaches the enable to link the knowledge economy with the knowledge management practices of organizations, namely the dynamic capabilities approach, organizational learning theory and theories concerning the use of technology in organizations. Since each of these theories focuses on a specific part of these practices and establishing that these practices are applied in unison, it can be argued that integrating them is necessary to get a full theoretical account of organizations and their management in the knowledge economy. In other words, dynamic capabilities, organizational learning and the use of technology are all part of it. The present analysis provides the step toward an integrated model based on these three theoretical approaches. It should be noted, it was possible to link the chosen practices to these approaches, but that this is not to say that these are the only practices or theoretical notions that matter for understanding organizations in the knowledge economy. Nevertheless, since other practices could not be satisfactory covered in this analysis, this asks for additional research.

Additional research is needed in the following directions to overcome the empirical shortcomings of this paper (e.g. a reliance on cross-sectional data and analyzing secondary data). This first route to be explored relates to a move toward the knowledge economy. An underlying assumption in the literature is that economies are moving in the direction of increasing knowledge intensity. That this is the case is not disputed here. However, even though the current analysis points in the direction of changes in the structure and governance of organizations due to the spread to the knowledge economy, the cross-sectional data do not allow causal statements. Hence, longitudinal data are needed for future research to assess whether changes in the economy also translates into changes in organizations and their knowledge management. What is crucial in this respect is that the indicators for the knowledge economy as well as indicators for the knowledge intensity of the work practices are available for a sufficiently long period of time. One of the drawbacks may be that the focus will be somewhat narrower as this relaxes the possibility of constructing a longitudinal dataset. The second direction for future research means taking a different approach, namely a further advancement of the measurement of KIWPs. In this paper, it was possible to construct a scale based on secondary data enabling the assessment of the work practices. The next step is to strengthen this measurement. There are two points that need attention to achieve that. To begin, the indicators used here can be taken as the starting point to create an improved scale measuring KIWPs that are derived from the three theoretical approaches (dynamic capabilities, organizational learning and the use of technology). To develop these indicators, it is additionally required to assess whether the three theoretical approaches suffice for that purpose or that other theoretical approaches are necessary to include as well. The resulting measure should be richer in terms of content (covering KIWPs more broadly) as well as its theoretical base (and the possibility to generate an integrated model of these work practices). A third direction for future research concerns the fit between the environment and organizational practices. Whereas literature suggests that external fit should enhance organizational performance, it is still an open question whether this holds in the case of knowledge intensity of the economy.

Finally, some practical implications can be distilled from this paper. The first is that knowledge-intensive organizations and organizations in which knowledge intensity is increasing, are advised to look at the way in which they structure and govern their organization. Since autonomy, learning and technology are central feature of knowledge intense organizations, they could consider whether their practices are in place. In addition to that, it makes sense to have these work practices in bundles. Therefore, it makes sense to see whether autonomy, learning and technology are governed consistently in the organization. And, finally, as the results show that the economic environment matters for these practices, it is advised to have mechanisms to scan external development that are relevant for their KIWPs. The present study provides concrete ideas how to do that. Managers and consultants can use the measures of knowledge intensity and KIWPs to assess organizations. For example, they can ask the question whether the organization provides sufficient training to their workers, given the knowledge intensity of the environment in which it operates.

Research model

Correlation between Innovation Index and national mean of KIWPs

Descriptive statistics

Note(s): (a) Compared to the empty model

Source(s): European Company Survey (ECS) 2019, Eurostat, and European Innovation Scoreboard (EIS) 2020

20,672 establishments in 28 countries

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Published: 20 January 2022

The role of knowledge economy in Asian business

Shumaila Zeb 1

Future Business Journal volume 8 , Article number: 1 ( 2022 ) Cite this article

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The study examines the role of the knowledge economy (KE) in Asian businesses in 45 countries for 2000–2019. KE indicators include education, economic incentives, innovation, institutional regime, and information and communication technology. The business indicators used in the study are starting, doing, and closing business. The empirical analysis is carried out by applying principal component analysis (PCA) and instrument variable panel fixed effects estimator. The results proved that the KE indicators are essential to improve businesses in Asia. They help the economies to boost their business sector and help to fight against poverty and unemployment.

Introduction

For the last two decades, economies of the world have been striving to evolve themselves to become knowledge-based economies (KBE). KBE relies less on traditional sources like land, labor, and capital for asset creation, wealth maximization, and economic prosperity [ 6 , 7 ]. In today’s world, the formation of new knowledge, research and development (R&D) to promote innovation and technological advancements to achieve prosperity is the key to success [ 47 , 66 ]. Similarly, the governments’ economic incentive based on new knowledge creation stimulates economic development, favors entrepreneurship, and increases employment opportunities in the economy. These existing trends, based on the creation and dissemination of new knowledge, are welcomed in developing and emerging economies. These are considered as the backbone of a KBE [ 65 ]. Similarly, knowledge economy (KE) is an expression used in developed economies to explain critical factors of knowledge creation and its usage as a means of production to gain competitive advantage and enhance efficiency and productivity.

KE and KBE’s roots are originated from different economic theories. Like, Neo classical economics and idea of increase returns to scale led toward knowledge-based economy. Different theories like endogenous growth theory (Romer 1990), organizational theory [ 62 ], and the theory of transaction cost [ 38 ] laid foundation of knowledge-based economy. Indeed, knowledge is nowadays considered as a driving force for enhancing productivity, promoting economic development, and increasing economic performance. KE does not only refer to employing new means for the production of goods and services but it also requires a complete novel paradigm. This new paradigm involves innovation, new technology, and high skilled human force in every sector of the economy.

Innovation is considered as one of the potential determinants of economic growth and prosperity in the developed world. It is one of the important pillars of KE. However, it is observed that the capacity of innovation remains less in most Asian and African economies [ 19 ] and [ 5 ]. Globalization is another critical factor, which also plays an essential role in the KBE. The globalization of new technology creates more opportunities for development and growth in the developing and emerging economies [ 48 ]. However, the situation is a bit different in certain economies including Asian economies. They need to invest more in human capital and infrastructure to accommodate and sustain high technology-oriented ventures and industries. Therefore, developing countries need to cooperate more with the developed world to increase their competitiveness and international trade goals [ 32 , 39 , 65 ].

In the light of the above, there are various studies on the relevance of KBE and KE. Several studies have been conducted on the relevance of human capital. Examples of these studies include Schultz [ 58 ] and the creation of growth models by Romer [ 55 ] and Lucas [ 41 ] made knowledge and human capital significant factors for growth. The roots of KE go back to the industrial revolution from the 1760s to the 1850s, when technological innovation boosted economic expansion [ 36 ]. Traditionally, there was more dependence on labor and capital as the primary sources of economic growth and development. However, the past two decades witnessed the vital role of KE in developed and developing economies [ 10 ]. No doubt, Asian countries are self-sufficient in the labor force [ 43 ]. However, now trends have changed due to technological advancements in producing goods and services. Asian economies contribute significantly in the GDP of the world. As per the income distribution, few Asian economies are at middle-income levels. Therefore, the Asian economies need to develop a KBE culture to enter into high-income levels like other developed countries. They must devise policies to improve their information infrastructure, communication and information, and human capital to become successful KBE [ 52 , 52 ].

The prominent examples of KBEs in Asia are the Republic of Korea, Singapore, China, India, Japan, Singapore, and Indonesia [ 65 ]. China is spending about 2% of its GDP on innovation [ 31 ]. Indonesia is spending considerable investments in education and innovation. India is also contributing toward promoting knowledge-based culture to boost its economy. During the past decade, Bangladesh has shown tremendous progress in information and communication technology (ICT) which opened doors to many industries. During the past few years, Sri Lanka turned out to have the highest KE index followed by India, Nepal, and Pakistan. The Republic of Korea’s customized training programs prompted fruitful business and human capital formation [ 61 ]. All these efforts confirm the value and importance of KE for sustainable, long-lasting economic growth and development.

The above sets of the narrative are consistent with the need to assess potential factors helping to encourage the KBEs in Asia. However, when we look at the empirical literature, we find that there is relatively less literature available on assessing the KE factors in relevance to the business development in Asia. Yet, for making effective and sound policies to promote and harvest the benefits of KE, it is important to empirically know how KE indicators are significant for boosting businesses in Asia. Therefore, this study provides first-hand empirical evidence and is vital because of the following reasons. Asian economies are striving to achieve a higher level of KE. They have a high potential to improve their existing knowledge and become more competitive to enhance their economic development and adapt globalization challenges. Further, KE helps the economies to enhance the quality of life of a common man, provide better investment and employment opportunities resulting in higher and sustainable economic growth. Therefore, the main objective of the study is to examine the role of KE in Asian businesses in 45 countries for the period 2000–2019. KE dimensions are reduced using the PCA to control the issues of multi-colinearity and over-parameterization. The other estimation technique used in the study is instrumental variable panel fixed effects. To cater to the complete life cycle of business, the business is subdivided into three categories, i.e., starting business, doing business, and closing business. The four KE components of the World Bank are used in the study. These components are education, innovation, economic incentives & institutional regime, and information and communication technology. The results provide strong evidence that education, economic incentives, and ICT help to decrease the time and cost of starting a new business. Further, the results show that innovation and inflation increase the time to start a new business. Similarly, education and innovation help decrease the trade barriers while doing business. It is also observed that ICT helps to lower the cost of exports in doing business. Economic incentives reduce business closure and insolvency. This empirical investigation is unique in its contribution. It contributes to the limited literature available on the Asian KBEs and the increasing challenges of enhancing business development using KBE and KE.

The remainder of the study is organized as follows. The relevant literature is discussed in Section II. Section III elaborates the data, sample selection, and variables description. Section IV presents the methods used in the study. Results and discussion are given in Section V. Conclusion and policy implications are discussed in the last section.

Literature Review

There are a number of empirical studies regarding the effects of KE dimensions on economic expansion [ 61 ]. For example, Hadad [ 28 ] proved that KE prompts economies to have vibrant and flexible schemes depending on state-of-the-art production elements, influences e-commerce, and other technological advancements while increasing economic benefit. According to Barkhordari, Fattahi, and Azimi [ 12 ], new knowledge creation and dissemination always lead to prompt a dynamic competition. In a recent study, Asongu et al. [ 11 ] discussed business dynamics, KE, and economic performance of 53 African countries from 1996 to 2010. Their main findings indicated that the dynamics of starting and doing business and changes in KE are strongly correlated to each other. Furthermore, a weak correlation is observed between KE and the economic performance of the underlying countries.

The knowledge that comprises reliability, specialization, and contestability features are considered to be powerful [ 70 ]. Harris and Ormond [ 29 ] concluded that relevant policymakers must focus on understanding and making adequate knowledge frameworks rather than pushing education through generic terms. In the context of Europe, Raspe and Van Oort [ 53 ] analyzed the contribution of knowledge toward economic growth. They used three main components of KE: knowledge workers, innovation, and R&D along with several other variables. They revealed that innovation and knowledge workers were more related to economic growth. According to authors, policymakers must preferably consider all three components while devising any regulation.

Despite an extensive literature showing the significant impact of KE on economic growth, Liargovas and Repousis [ 40 ] found some interesting results in Greece from 2007 to 2013. They used knowledge capital, business capital, and the stock of physical and labor capital in 51 Greek regions. The results demonstrated that in comparison with knowledge capital, business capital has a more profound impact on economic growth. They recommended that policymakers and regulators must also consider entrepreneurship as a tool for spreading knowledge. In another study, Bogoviz et al. [ 13 ] investigated the role of human in the economic system under the conditions of KE in Russia. They used correlation analysis to find the dependence of KE on different types of resources, i.e., technological, human, material, and investments for the period 2010–2016. They concluded that KE provides more opportunities in the source of creation, implementation, and dissemination of innovational goods. This leads humans to become active innovational entrepreneurs.

In the African countries’ context, Amavilah et al. [ 3 ] investigated how globalization affects peace and stability through the channel of governance and KE. The authors used the instrumental variable panel fixed effects estimation econometric approach for a sample of 53 African countries for the period 1996–2010. They found that sustainability in KE can only be achieved if African countries pursue such globalization policies that result in peace and stability. Similarly, Muzaka [ 47 ] conducted a study on competitive KE in two emerging states India and Brazil for the 1990s. The findings revealed that strong nationalist sentiments are the pillars on which India and Brazil built this new orientation toward becoming successful KBE. However, these types of sentiments are not present in advanced economies. Likewise, Dima et al. [ 21 ] analyzed the relationship between KE and global competitiveness in the European Union (EU). They used various indicators of KE such as R&D expenditure, lifelong learning, GDP per capita, percentage of the population with tertiary education, and debt to equity. They found that innovation and education play a crucial role as predictors of economic growth. However, they concluded that focus on R&D activities and lifelong learning possibilities could significantly contribute to competitiveness in EU member states.

Similarly, the role of new knowledge capital in firm production and industrial growth is analyzed by Woods et al. [ 69 ]. He used two productions and one learning model. He concluded that a firm might experience a decrease in knowledge capital under diminishing returns. He found that when a firm updates its knowledge capital, it not only increases its productivity but it also has a spillover effect on the whole industry. Nurunnabi [ 50 ] examined how Saudi Arabia is swiftly transforming itself into a KE. The findings demonstrated that despite the rapid transformation of Saudi Arabia into a KBE from the last few decades, some steps are needed to be implemented to avail full-scale advantages of KE. He further suggested that Saudi Arabia must increase its GDP allocation toward further R&D process. Also, reducing unemployment in female graduate students and rising human capital are some key issues that need attention from the relevant authorities.

Several studies proved that education helps to improve the KE level in any economy. For example, Evoh et al. [ 22 ] analyzed how different aspects of KE, especially higher education institutions, and the application of ICT innovations influence capacity development in Africa. They used different learning institutions in Kenya and Uganda as case studies. The results revealed that knowledge production for the advancement of African economies is not fulfilled by the higher education system. They further suggested that higher education institutions must engage in design-driven innovation and employ public–private initiatives in universities and research institutions.

On the relevance of education in the context of the global economy, Bogoviz et al. [ 14 ] analyzed the regularities and tendencies of globalization of education required in KE in nine different economies. They used various qualitative and quantitative measures and found a significant association between multiple indicators of KE on the globalization of education. They discovered that globalization of education increased the creation of KE. The results revealed that from 2016 to 2018, the tendencies showed decreased foreign lectures and an increase in international students. Similarly, Hassan and Cooray [ 30 ] proved the vital role of education in economic growth. They examined the effects of school enrollment on economic development taking a variety of gender groups from the Asian perspective. They found that the results of education are considered positive for both males and females at all educational levels including primary, secondary, and tertiary ones.

Wantchekon et al. [ 67 ] found that the benefit of educating one generation can develop a better attitude toward education and learning in the coming generations. Even educated relatives can transfer their knowledge to extended family members. This transfer of knowledge was not seen in the uneducated family and friends. In terms of Asian countries, Hongyi and Huang [ 33 ] conducted a study on health, education, and economic growth in China. They used a panel data set of 28 provinces in China for the period 1978–2005. After employing panel regression two-stage least square regression analyses, they found that both education and health are positively associated with economic growth.

Similarly, Gyimah-Brempong et al. [ 27 ] analyzed the role of higher human education capital on the economic growth of African countries using a dynamic panel data estimator from 1960 to 2000. The findings revealed that the growth rate per capita income is significantly related to higher education human capital. Their results imply that both economic growth and education human capital rely on physical capital investments. In another study, Moodie and Wheelahan [ 46 ] criticized that generic education delivered is a product rather than a process by which knowledge can be derived. He further argued that the curriculum approach of teaching knowledge must be altered from generic to disciplinary methods. This would help intelligent knowledge users. He further argued that intelligent knowledge users could learn through history. They can easily relate past knowledge to solve problems of the present and future.

Innovation is considered to be one of the most important elements that contribute to KE and business performance. The expansion of the global economy rests upon the fundamental strands of open innovation. Thus, the creation, use, and management of knowledge drive both competitiveness and productivity [ 68 ]. In the innovation dimension, Agénor and Neanidis [ 1 ] found that an extra innovation routine enhances economic growth. It was studied in the role of R&D spending in the economic progress of 66 economies during the period 2000–2009. The results found that R&D spending positively affected the growth in upper-middle-income countries [ 34 ]. Castellacci and Natera [ 15 ] also proved positive relationship between strong innovation policies and economic development.

Gabriele et al. [ 25 ] analyzed the R&D collaborations in the regional context. The findings elaborated that knowledge represented by public research institutions is the primary source that firms use for collecting knowledge. Furthermore, they found that smart firms acquired knowledge from sources outside the region and did not primarily rely on local knowledge hubs. In another study, the relationship between transmission power and indicators of KE in six OECD countries (USA, Canada, France, Germany, Japan, and South Korea) is analyzed by Mêgnigbêto [ 44 ]. The results indicated that for 2001–2010 in South Korea and Japan, there is a strong positive correlation between gross domestic expenditure for R&D and transmission power.

Information, communication, and technology have been game-changer in KBE. Firms can achieve sustainable competitive advantage in high-tech industries. By keeping in mind this perspective, Martín-de Castro [ 42 ] explained one of the most complex business phenomena in the form of a firm’s technological advantage. He proved that a firm could never achieve higher strands of innovation in isolation. External relationships develop better and faster innovations. Das et al. [ 17 ] and Jorgenson and Givens [ 37 ] proved that ICT is positively related to economic development. They concluded that ICT investment had a positive and significant effect on the development of the global economy.

Barkhordari et al. [ 12 ] found that the MENA region is investing its revenue in construction schemes, ICT, and good health facilities to improve its economic condition. He further added that strong financial institutions would favor investing in new technology to enhance economic growth. In terms of Asian countries, Ahmed and Ridzuan [ 2 ] used the panel estimation approach to investigate the impact of ICT on East Asian economic growth from 1975 to 2006. They used ICT investments, capital, and labor as independent variables while the real gross domestic product (GDP) is used as a dependent variable of the study. The findings indicated that investments in ICT products are positively and significantly related to GDP. They recommended that East Asian countries must invest more in ICT products to achieve sustainable growth in the long-run period.

Datta and Agarwal [ 18 ] used data for 22 OECD countries and analyzed the long-run association between telecommunication infrastructure and economic growth. They revealed that both variables are positively and significantly related to each other. Similarly, Roller and Waverman [ 54 ] found that demand for telecoms is significantly and positively associated with GDP for 1970 to 1990 for a sample of 36 countries. In the case of Singapore, Poh, Ang, and Bai [ 51 ] analyzed the impact of ICT investments on productivity from 1977 to 1997 using Cobb–Douglas production. The main findings indicated that productivity maintained a positive and significant relationship with ICT investments. Similarly, Niininen [ 49 ] also concluded that ICT has a substantial impact on real growth output in Finland.

Shahbaz et al. [ 60 ] analyzed the relationship between ICT and electricity demand in the UAE by using co-integration for 1975–2011. They found that electricity consumption increased by the use of ICT. The causality analysis proved that electricity consumption does not Granger causes ICT, but the same is true for the opposite side. However, electricity prices Granger causes both economic growth and ICT. They suggested the use of smart ICT infrastructure and an increased focus on energy-efficient R&D policies help to achieve sustainable economic growth. In the same line, Sadorsky [ 57 ] analyzed the impact of ICT on electricity consumption. They used Internet connections, mobile phone users, and the number of personal computers as a proxy for ICT. The findings demonstrated a positive association between ICT and electricity consumption. Likewise, Ishida [ 35 ] found a long-run stable relationship between the demand for energy and production function from 1980 to 2010 in Japan. They used multivariate models related to demand for energy and production function. Moreover, comparative analysis between emerging and G7 countries on various aspects of KE and total factor productivity (TFP) is done by Shahabadi et al. [ 59 ]. They used panel data analysis for the period 1996–2013. The results indicated that the ratio of ICT capital stock to GDP and the ratio of foreign R&D capital stock to GDP have the greatest positive impact on TFP. The results largely imply that emerging economies adopt new production factors such as domestic R&D and innovation while leaving behind traditional production factors.

Political institutions make economic institutions. The strategies and policies made by the economic institutions result in economic growth. Hence, it concludes that political institutions provide the impetus for economic development [ 23 ]. Similarly, Asongu and Andrés [ 9 ] found that the absence of adequate credit facilities harms the growth of KEs in Africa and Middle East countries. In respect of the innovation aspect, they noted that the lack of technical and scientific publications is restricting these economies from obtaining the full benefits of KBEs. The finding further proved that the time required for full convergence from low to high level of KE is about 4 to 7 years.

Data Description, Sample Selection, and Variables Description

The recent study uses a panel of 45 Asian countries. Initially, all Asian countries were used as a sample of the study. However, Lebanon, Japan, and Turkmenistan are excluded from the sample due to the non-availability of data. List of the countries included in the sample is given in Appendix A-1. The data begin from 2000 because for Asian countries, data for education and the institutional regime were available from this year. The annual data related to the variables used in the study are extracted from the World Bank indicators (WDI) for the period 2000–2019.

Following previous literature Tchamyou [ 65 ], we classified business into three categories, i.e., starting, doing, and closing the business. The three phases described in the study are used to cover the complete life cycle of a business. The starting business includes three indicators. They are time required to start a new business, the cost of starting a new business, and the number of newly created businesses. The doing business phase indicators include trade openness, technology exports, and property rights institutions. The ending business phase includes the time required to resolve insolvency. These business indicators are used as dependent variables. A detailed description of the dependent variables is given in Table A-2. The World Bank’s KE indicators show a complete picture of all aspects of an economy [ 64 ]. Therefore, following the World Bank, we also used the same indicators, i.e., innovation, education, institutional regime & economic incentives, and ICT as independent variables. Due to extensive issues of multi-collinearity and over-parameterization, we derive each dimension of KE using the PCA. Each dimension of KE used in the study is given in Table A-2.

We also control for the macroeconomic indicators. Only those indicators are used which might influence the different phases of the life cycle of a business. These are inflation, government expenditure, and GDP growth. Inflation is expected to be negatively associated with the business. GDP growth is expected to be positively associated with all the indicators of the business. However, government expenditure depends upon a number of other factors, including budget allocation and misallocation or malpractices like corruption. Therefore, we cannot predict its sign. Empirical definitions of the variables are given in Table A-2.

KE indicators are used as independent variables of the study. It is expected that all independent variables are correlated with each other or with the component variables. Therefore, it requires employing an estimation technique that tends to reduce a large number of correlated variables into a small set of uncorrelated variables known as principal components. Therefore, we used the PCA following Tchamyou [ 65 ] and Asongu [ 8 ] for each of the KE indicators. They also recommend dropping the factors having an Eigenvalue less than one. Therefore, each of the dimensions of KE is reduced by PCA to produce various indices. Education included primary school enrollment, secondary school enrollment, and tertiary school enrollment. Education is reduced by the use of the PCA to Educatex index. Similarly, ICTex, Creditex, Instireg, and Innovex indices are also created using the PCA.

We used instrument variable panel fixed effects following Tchamyou[ 65 ]. Two stages of regressions are estimated for each variable. In the first-stage of regression, we regress the KE indicators separately on their first lags with robust heteroskedasticity and autocorrelation consistent (HAC) standard errors. The fitted values obtained from the first regressions are then used in the second stage of regressions. The second-stage regressions are also HAC and further controlled for the unobserved heterogeneity and multi-collinearity to avoid biased estimates.

KE is presented by Educatex, ICTex, Creditex, Instireg, and Innovex indices created by the PCA. Instruments are used in the first equation are lags of the endogenous variables. Business is further divided into starting, doing, and closing business. Separate regressions are estimated for each category of business. Control variables used in the study are inflation, GDP, and government expenditures. $v_{it}$ and $e_{it}$ are the error terms. $\varepsilon_{t}$ is the time-specific constant.

Results and Discussion

This section presents the summary statistics and regression results of the study. Table 1 presents the summary statistics of the variables used in the study. The value of the standard deviation of different variables shows a significant variation. Therefore, we are confident that a few important estimated relationships would be derived from the estimations. The negative mean of Education indicates that the Education level in Asia is very low. Efforts are required to increase the education level of the Asian economies. Similarly, Information and technology, and credit enhancing facilities from the financial institutions to the other sectors of the economies also need to be improved in these economies. The mean value of innovation indicates that there is less innovation in Asian economies. The standard deviation to of all the components of starting a business shows a high variation in starting the business in Asian economies.

Tables 2 , 3 , and 4 present the estimated coefficients obtained from the regression analyses. A separate regression is estimated for each business phase. The estimated coefficients are presented in the tables followed by the standard errors in parenthesis. The information required for the validation of the models used falls under the acceptable criterion. The F-statistics indicate that all the models used in the study are fit and valid. Overall, all the models have a high significance level of 1%.

The study used four business indicators to explain a business start-up. The estimated coefficients of time to start a business and cost to start a business suggest that both variables are negatively and significantly related to education. The results suggest that education helps individuals to decrease the time and cost to start a business. However, the results also show that education is significantly and positively related to the new business density. The result implies that education helps to increase business density. The result also implies that students who undertake certain entrepreneurial activities during their undergraduate and graduate programs help to improve the poverty level of the economy. The education also improves the negotiation and conflict management of the students which helps them to reduce the time and cost to start a business. The results are consistent with the findings of Singh [ 63 ] and Gerba [ 26 ].

The estimated coefficient of economic incentives shows that it is negatively and significantly related to the time to start a business. The result also shows that an economic incentive is positively and significantly related to the new business registrations as well. The results imply that economic incentives favor new business ventures in the economies. It helps to reduce the time to start a new business and increase the number of businesses in an economy. The estimated coefficient of Creditex shows that if everything remains equal, a one-unit change in the economic incentive will decrease the time to start a business by 0.85 units on average. Similarly, the estimated coefficient of Creditex shows that everything remains equal; a one-unit change in the economic incentive will increase the number of businesses by 0.96 units on average. The results follow Wantchekon et al. [ 67 ] and Mensah and Benedict [ 45 ].

The estimated coefficient of innovation shows that it is positively and significantly related to starting a business and new business registrations. The results further suggest that innovations in any economy open new gateways in the economy. This result implies that innovation in an economy helps boost the new business start-ups in the country. However, in starting a business, the estimated coefficient shows that innovation decreases the new business density as well. The estimated coefficient of the institutional regime suggests that it is positively related to the time to start a business and business registration. However, the estimated coefficient of institutional regime indicates that it is negatively related to the new business density. This result implies that because of the large bureaucratic culture of Asia, the institutional regime results in increasing the time to start up a business and decreases the business density. The results are similar to the findings of Asongu [ 6 , 7 ] and Chavula and Konde [ 16 ].

The estimated coefficient of ICT suggests that it is negatively related to the time to start a business and positively related to the number of business registration. These results imply that ICT helps to reduce the time to start a new business venture. However, ICT increased the time to register the business start-up. This finding implies that maybe the population of Asia is still not technology-oriented. People are still struggling to accept the new paradigm of Information and technology. There is also another reason for this result that the Internet and communication connectivity in Asia is still not that good in comparison with the European and American countries. The results are similar to [ 6 , 7 , 20 ]. The results prove that education increases technological learning and favors business growth.

The estimated coefficient of inflation shows that it is positively related to the time to start a business and negatively associated with the business density. The result suggests that inflation can result in administration delays and can incur other costs on the projects. The estimated coefficient of government expenditure is negatively related to the cost of doing business and business density. This means that government expenditure reduces the cost of doing business. This means that governments are extending more funds to the private sector. Similarly, the GDP growth is negatively related to the cost to start a business and positively related to the new business density. This result implies that economic prosperity in the country helps the business community. Economic prosperity reduces the time and costs to start a business.

The second phase of regression is for doing the business. The estimated coefficient of education shows that it is positively and significantly related to the cost of exports and negatively related to the trade barriers. The results show that economic incentives lead to increase in trade barriers and decrease in the cost of exports. The results also indicate that the institutional regime was not significantly related to doing business. All results are statistically insignificant for doing the business. The estimated coefficient of ICT shows that cost of exports and trade tariffs is negatively and significantly related to ICT. The cost of exports is reduced due to decrease in cost of information. The ICT helps to reduce the trade tariffs as well due to decrease in the cost of information and increase in competition in an economy. The control variable inflation shows that an increase in inflation reduces trade openness and trade tariff.

The estimated coefficient of education indicates that it increases the cost of exports and reduces trade tariffs. The estimated coefficients of education reveal an insignificant relation with Trade openness, ICT goods export, and high-technology exports. Furthermore, the estimated coefficient of education shows that it is positively and significantly related to the ICT Services export. An increase in education also increases the ICT Services exports. The estimated coefficient of economic incentive proves that its increase results in the decrease in cost of exports and trade barriers and increase in ICT Services exports. The results are following the findings of Suh and Chen [ 64 ]. ICT also affects the cost of exports and trade barriers negatively. The result implies that more ICT within an economy results in a decrease in cost of exports and trade barriers. This implies that it favors the businessmen by reducing the cost of exports and trade barriers. The results further proved that export of ICT goods and high-technology exports are increased by ICT. It means that an increase in the ICT helps to increase the ICT goods exports and high-technology exports.

About innovation in the economy, the estimated coefficient of Innovex shows that it increases ICT goods export. However, the results further show that innovation decreases trade barriers; IT services export and high-technology exports. The estimated coefficient of inflation shows that it is positively and significantly related to the cost of exports. The result implies that high inflation results in more uncertainty in the prices and interest rates. Inflation increases ICT goods exports and high-technology exports. Government expenditure is negatively related to the cost of exports and trade tariffs. GDP growth is positively related to ICT goods export and high-technology exports. The estimated coefficient of education shows that it is negatively related to contract enforcement. The economic incentives lead to increase the investment protection disclosure. The results further prove that institutional regimes decrease the time of registration of property. The estimated coefficient of inflation shows that it is negatively and significantly related to the contract enforcement and registration of property.

Education decreases contract enforcement time. ICT is negatively related to contract enforcement time and private property registration time. However, it is positively and significantly related to business disclosure. The institutional regime has a negative impact on the registration of property and business disclosure. The results are similar to Fosu [ 24 ] and Andrés et al. [ 4 ]. GDP growth is negatively associated with business disclosure. Inflation decreases contract enforcement and registration of property time. The estimated coefficient of education, economic incentives, innovation suggests that these variables have no significant impact on closing the business. The institutional regime is significantly and negatively related to the closing of the business. The results are similar to [ 6 , 7 , 65 ]. The GDP growth is also showing a negative but significant relationship to the closing of the business.

The study has scrutinized the role of KE in Asian business using the data of 45 countries for the period 2000–2019. The KE indicators used in the study are education, innovation, economic incentives, and institutional regime, and ICT. The business indicators are classified into starting, doing, and closing business. The KE indicators are reduced using the PCA to control the issues of multi-colinearity and over-parameterization. The estimation techniques used in the study are instrumental variable panel fixed effects. The importance of the research lies in the fact that the literature on the KE and business is scarce in Asia. This study adds to the literature of growing challenges of improving the Asian business climate, emphasizing KE. The findings reveal that education and ICT help to decrease the time and cost to start a business. Economic incentives also help to reduce the cost of starting a business but also increase the business density and the number of businesses. Innovation also helps to increase the number of businesses and business density as well. The findings further suggest that institutional regime decreases the chances of a business closure. GDP growth is also showing a negative but significant relationship to the closing of the business.

The results suggest that Asian countries need to invest more in education. There is a lack of investment in the education sector. The findings suggest a dire need to improve knowledge infrastructure, human skill development, the relationship between science and industry, and support for R&D. Therefore, Asian countries need to improve their existing educational strategies to obtain more benefits from starting and doing business. There is a need to pay more attention to small ventures, business start-ups, and entrepreneurial lesions in the management specializations. The policymakers and regulators must foster high-caliber scientists and engineers capable of handling growth and challenges in technology and science. Similarly, ICT policies need to be articulated by improving human resources, entrepreneurial activities, R&D, market liberalization, and privatization, and the construction of advanced infrastructure. The rigorous R&D programs are important for the success of Asian countries. Innovation, with a strong ICT, economic incentives coupled with highly skilled labor, and human resources lead to an increase in the businesses in the economy results in the growth of the industry. There must be certain relaxation in the property rights in order to facilitate innovation in the business climate. The policymakers may confront the corruption in business circles while improving institutional quality. Hence, credible and effective government strategies are essential to attain long-run business development.

This study is one of the pioneer studies to investigate the role of KE on Asian businesses. Future research can be done to compare the differences in the role of knowledge economy in the different countries of Asia.

Availability of data and materials

The data can be made available upon request.

Abbreviations

Knowledge-based economy

Knowledge economy

Research and development

Information and Communication Technology

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Acknowledgements

I would like to acknowledge our Head of the Campus Sir Khusro Pervez Khan and our Head of Department Dr. M Asif Khan for always being a source of motivation and inspiration.

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The main contribution of this study is that it adds in the literature of growing challenges of improving the Asian business climate with a major emphasis on Knowledge Economy. The findings of the study open new doors for the policymakers, regulators, and the management of the business community with more emphasis on Knowledge Economy. The author read and approved the final manuscript.

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Getting the Job Done: How Immigrants Expand the U.S. Economy

September 8, 2020 • 4 min read.

Immigrant workers put pressure on the U.S. labor supply, but foreign-born entrepreneurs also create jobs that increase labor demand, according to new research co-authored by Wharton's Daniel Kim.

Markets & The Economy
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Wharton’s Daniel Kim speaks with Wharton Business Daily on SiriusXM about his research on immigration and entrepreneurship in the U.S.

In the United States, the economic impact of immigration is a lightning-rod topic that sparks strong feelings on both sides. Opponents have long held that immigrants take away jobs from American citizens and lower wage standards. Proponents dismiss that idea, saying immigrants expand the economy through their hard work and determination. The truth is somewhere in the middle, according to new research from Wharton’s J. Daniel Kim.

To be sure, immigrant workers ramp up competition for jobs, creating a surplus in labor supply for some sectors. But immigrant entrepreneurs have a more profound impact on overall labor demand by starting companies that hire new workers, creating a positive ripple-effect on the economy.

“The problem with the ongoing discussion is that it’s largely one-sided,” Kim said in a recent interview with the Wharton Business Daily radio show on SiriusXM. (Listen to the podcast at the top of this page.) “To be fair, both forces here simultaneously exist. In order for us to have a systematic understanding of the role of immigration on job creation, you need to take both accounts together. And this is what we do in the study.”

Kim is co-author of “ Immigration and Entrepreneurship in the United States ,” along with Pierre Azoulay , professor at MIT’s Sloan School of Management and associate with National Bureau of Economic Research (NBER); Benjamin F. Jones , professor at the Kellogg School of Management at Northwestern University and an associate with NBER; and Javier Miranda , economist with the U.S. Census Bureau. In their research, the scholars use comprehensive administrative data from 2005 to 2010 on all new firms in the U.S., the U.S. Census Bureau’s 2012 Survey of Business Owners, and data on firms listed in the 2017 edition of the Fortune 500 ranking to paint a more accurate picture of the economic impact of immigrants in America.

“The problem with the ongoing discussion is that it’s largely one-sided.”

“This paper works to fill in the picture through the lens of entrepreneurship,” the authors wrote. “By looking in a more comprehensive manner at the U.S. economy, the analysis helps balance the ledger in assessing immigrants’ economic roles.”

Dispelling Myths

Immigrants make up roughly 15% of workers in the U.S., yet they are 80% more likely than native workers to become entrepreneurs, according to the study. By those numbers, the assumption that immigrants leach jobs away from Americans isn’t incorrect, but it is incomplete. First- and second-generation immigrants are launching businesses across the spectrum, from small sandwich shops with one or two employees to major tech firms with thousands of workers. For example, when South Africa native Elon Musk built his Telsa plant in California, he spawned more than 50,000 jobs and injected $4.1 billion into that state’s economy in 2017.

“What we find, with overwhelming evidence, is that immigrants act more as job creators than they act as job takers in the United States,” Kim said during his interview with Wharton Business Daily.

“Immigrants in the U.S. create a lot more jobs than they take, primarily because many are prone to starting businesses that go on to create a lot of jobs.”

The study builds on previous research that dispels myths about immigrant workers and quantifies the facts, including that immigrant entrepreneurs account for close to 25% of patents and are more likely to hold STEM degrees. Using tax records, the researchers debunked another popular theory that immigration suppresses wages. They found wages were the same or slightly higher for immigrant-founded firms versus firms with native founders.

The authors encourage more research along the same dimensions, saying more information can help shape economic policy around immigration and help remove politics from a debate that’s often short on truths.

“That’s the main takeaway here, that immigrants in the U.S. create a lot more jobs than they take, primarily because many are prone to starting businesses that go on to create a lot of jobs,” Kim said. “While I will not comment on the policy implications of these results, I believe that the broader discussion on the role of entrepreneurship and immigration on economic growth needs to account for both sides – because leaning on one would provide an incomplete picture.”

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ORIGINAL RESEARCH article

The interactive effect between economic uncertainty and life history strategy on corrupt intentions: a life history theory approach.

1 Fudan University, Shanghai, Shanghai Municipality, China
2 Department of Psychology, Fudan University, Shanghai, China

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Why do some people show more corruption when facing uncertain environment?The present study aimed to give a plausible answer from an evolutionary perspective: this might be rooted in people's different life history strategies (slow vs. fast). The present study measured the participants' corrupt intentions by a hypothetical scenario and primed the feeling of economic environmental uncertainty by requiring the participants to read economic uncertainty (vs. neutral) materials. It is revealed that the participants with fast life history strategies had stronger corrupt intentions after reading materials about economic uncertainty than reading neutral materials. In addition, the desire for power mediated the interactive effect between life history strategy and economic uncertainty on corrupt intentions for fast life history strategists. This finding was discussed for its theoretical and practical implications from the perspective of life history theory.

Keywords: Economic uncertainty, life history strategy, Desire for power, corrupt intentions, evolutionary psychology perspective

Received: 25 Dec 2023; Accepted: 11 Apr 2024.

Copyright: © 2024 Sai and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Lei Zhu, Department of Psychology, Fudan University, Shanghai, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Economic conditions outlook, March 2024

Executives’ latest views on the global economy and their countries’ economies lean much more positive than they did at the end of 2023.

In the latest McKinsey Global Survey on economic conditions, 1 The online survey was in the field from March 4 to March 8, 2024, and garnered responses from 957 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP. the outlook on domestic conditions in most regions has become more hopeful, despite ongoing shared concerns about geopolitical instability and conflicts. In a year brimming with national elections, 2 Katharina Buchholz, “2024: The super election year,” Statista, January 19, 2024. respondents increasingly see transitions of political leadership as a primary hazard to the global economy, particularly in Asia–Pacific, Europe, and North America.

Furthermore, respondents now view policy and regulatory changes as a top threat to their companies’ performance, and they offer more muted optimism than in December about their companies’ prospects.

Optimism builds over global and domestic conditions

Respondents share much brighter assessments of the global economy and conditions in their countries than they did at the end of 2023, and views of the global economy are the most positive they’ve been since March 2022 (Exhibit 1). In the December survey, respondents were equally likely to say the global economy had improved and worsened. Today, respondents are twice as likely to report improving rather than deteriorating conditions. Looking ahead to the next six months, respondents are also more optimistic than they were last quarter. Forty-six percent expect the global economy to improve—nearly double the share expecting worsening conditions—while 37 percent expected improvement in the previous survey.

Likewise, respondents offer hopeful views when asked about the most likely near-term scenario for the global economy, suggesting confidence in central banks. They are more likely to expect a soft landing overall—with either slowing or accelerating growth compared with 2023—than a recession (Exhibit 2). The largest share of respondents expect a soft landing, with slowing growth relative to 2023.

Respondents’ views on their own economies have also become more upbeat. Nearly half of respondents say economic conditions at home are better now than they were six months ago, up from 41 percent in December, while just 22 percent say conditions have gotten worse. Respondents in Europe—who offered the most negative assessments of any respondents in September and December—are now nearly twice as likely as in December to say conditions have improved in the past six months, though it is unclear what has prompted that change and whether it is a durable finding.

McKinsey’s original survey research

More than half of respondents expect their economies to improve over the next six months. It’s the first time in two years that a majority of respondents have said that. In most regions, larger shares of respondents express optimism about economic conditions at home now than in December (Exhibit 3).

Geopolitical instability remains top of mind as concerns over political transitions rise

Geopolitical instability and conflict continues to be the most cited risk to global growth, selected by two-thirds of respondents for the second quarter in a row (Exhibit 4). Yet in this first quarterly survey of 2024—a year in which more than 60 countries will hold national elections 3 Katharina Buchholz, “2024: The super election year,” Statista, January 19, 2024. —transitions of political leadership have jumped from the fifth-most-cited to the second-most-cited threat to the world economy. The share of respondents in Europe reporting political transitions as a top threat is 2.4 times the share in December, while the shares in North America and Asia–Pacific have nearly doubled. 4 Prior to the latest survey, respondents in Mexico were included in Latin America in analyses but are now included in North America. We see a smaller uptick in concern about supply chain disruptions, which is cited as a threat by the largest share of respondents since December 2022.

Looking at risks to growth in respondents’ countries, geopolitical instability and conflict remains the top perceived threat, cited by a larger share than in any quarter since March 2022. Uneasiness about domestic political conflicts and transitions of political leadership, now the second- and third-most-cited risks, have overtaken concerns about inflation, which was the second-most-cited risk in December. Among respondents in North America, transitions of political leadership are cited nearly twice as often as in December (Exhibit 5). In Greater China, multiple risks now appear to carry equal weight, whereas in December, inflation was the top concern.

Policy and regulatory changes top the list of cited threats to companies’ growth

As respondents’ concerns about inflation as a domestic threat wane, the survey results suggest that companies are holding off on price increases. For the first time since we began asking about companies’ prices in September 2022, less than half of private-sector respondents in the latest survey—45 percent—say their companies increased the price of their goods or services over the past six months, down from 56 percent in December.

For five quarters, respondents’ most cited risk to their companies’ performance in the next 12 months was weak customer demand. Now, they most often point to policy and regulatory changes as a threat. In December 2023, policy and regulatory changes weren’t even one of the top five perceived risks. This increased wariness of policy changes cuts across most regions, though we see the largest increase in Europe.

Even though weak demand is no longer the most cited risk for companies, optimism over expected demand has tapered since December. Fifty-one percent of respondents expect an increase in customer demand over the next six months, down from 57 percent in December. Yet expectations about profits remain upbeat: about six in ten respondents expect increasing profits in the months ahead, in line with expectations in much of 2023.

The survey content and analysis were developed by Jeffrey Condon , a senior knowledge expert in McKinsey’s Atlanta office; Krzysztof Kwiatkowski , a capabilities and insights expert in the Boston office; and Sven Smit , chair of insights and ecosystems, chair of the McKinsey Global Institute, and a senior partner in the Amsterdam office.

They wish to thank Jan Mischke for his contributions to this work.

This article was edited by Heather Hanselman, a senior editor in the Atlanta office.

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A Critical Review of the Precursors of the Knowledge Economy and Their Contemporary Research: Implications for the Computerized New Economy

Kwee keong choong.

1 Apt Visionary & Innovation, Melbourne, Australia

Patrick W. Leung

2 Department of Accounting, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China

Despite nearly fifty-eight years since the term knowledge economy first appears, we are getting nearer in understanding this new kind of economy. The purpose of this paper is to clarify the meaning of the knowledge economy by conducting a critical review of the precursors of the knowledge economy and their major critiques so as to identify the current research implications. We aim to identify common ground in advancing the research of the knowledge economy. In essence, our understanding of the knowledge economy is viewed from a ‘new’ social-economic-theoretical perspective in which the theoretical foundation focuses on the explosion of technology that motivates people to be innovative and possess knowledge in producing knowledge products or be so engrossed with sociability using technology at home. Our finding is that the notion of the knowledge economy must be viewed from some phenomena that have transformed the contemporary economy. Other major findings include the following: (1) we use the term knowledge economy instead of the multiplicity of terms to describe this new form of economy; (2) we articulate that the theoretical foundation of the knowledge economy is a branch of social economy where the economy is not based solely on production and consumption but is based more on social values, technology, knowledge and innovation to commercialize knowledge products; and (3) the statistical assessment methodology is delivered through the use of indicators to proxy for the four knowledge economy criteria that makes up the knowledge economy. This has the following implications for economic management, knowledge-induced innovation, computerization of the economy, and knowledge management in the new economy.

Introduction

The field of the knowledge economy is receiving tremendous interest. Isenberg ( 2010 ) reported that there were 40,000 articles on the knowledge and knowledge-based economies as at 2010 and there is a multiplicity of terms being used to describe similar concepts related to knowledge management (Davenport, 2005 ; Nonaka & Takeuchi, 1995 ), knowledge economy (Drucker, 1969 ; 1973 , 1999 ), knowledge industry (Machlup, 1962 ; Machlup & Mansfield, 1970 ), information economy (Porat, 1977 ; Porat & Rubin, 1977 ) and network society (Castells, 1997 ; Van Dijk, 1999 , 2006 ). In view of the hefty of terms used, they can collectively be labelled as the knowledge economy (KE), and their authors are avowed as the precursors of the KE. However, nearly 58 years since Machlup’s seminal work on the KE, we are now getting nearer in understanding this new kind of economy. Initially, significant doubts arise as to whether the ‘modern economies’ are, indeed, ‘knowledge economies’. More critically, the current economic meltdown and the Covid-19 pandemic of these so-called knowledge economies are testimonies of false assertion by the precursors of the field, and most recently, the current medical pandemic has cast further doubt on the sustainability that the developed economies are knowledge economy. Such inadequacy occurs in view of the fact that the field has no shortage of writers and critics that advance or reject various avenues of the knowledge economy. So, what actually is ‘knowledge economy’?

The objective of this paper is to understand by questioning and reasoning by what the precursors meant by ‘knowledge economy’ and not on what they have explained in a casual manner. We also want to know whether similar terms like ‘knowledge-based economy’, ‘information economy’ or ‘knowledge society’ meant the same or different things, and even if they were used merely for semantic reasons, is there a common ground for us to specify a research agenda that enables us in the furtherance of the study of the field in a systematic manner? The objective is also to find the implications for economic management, computerization of the economy and knowledge management in the new economy. Therefore, this paper will make a contribution to the literature in the areas of the theory and management of the knowledge economy as it provides a new perspective of the issues in the emergence and operation of the knowledge economy, providing a better understanding of the computerized new economy.

In doing the above, we use a methodology comprising a systematic review, content analysis and critical theory (analysis) in helping us to examine the terms and contents used to describe a knowledge economy. The next section examines what the precursors of the knowledge economy have told us. In ‘ Commentary of the Precursors of the Knowledge Economy ’, we critically review what the critics of the precursors have told us, and explain what is wrong with the current reality of the knowledge economy. ‘ Theoretical consideration in advancing the research on the knowledge economy ’ is concerned with avenues in advancing the research of the knowledge economy. ‘ Conclusions and Discussion ’ reports the findings of the paper and their implications and concludes our discussion of this research.

Methodology

A systematic review of the literature.

According to Godin ( 2010 ), the works of the precursors of the knowledge economy can be divided into two waves of studies pertaining to the field. The works of Machlup, Mansfield, Drucker, Bell, Porat and Rubin are considered the first wave of studies on the knowledge economy, and the second wave started in the mid-1990s that revitalizes the studies in the first wave with a change in emphasis and continues today. The notable works of this second wave are Nonaka, Takeuchi, von Krogh, Davenport, Prusak and Volpel, Van Dijk, Castells, United Nations (UN), United Nations Educational, Scientific and Cultural Organizations (UNESCO) and the Organization for Economic Cooperation Development (OECD). Although the works of the UN, UNESCO and the OECD are not really seminal, aspects of their works have merits and, hence, can be considered as precursors of the second wave. This assertion by Godin ( 2010 ) is consistent to the Handbook on the Knowledge Economy by Rooney et al. ( 2005 ).

Since we already know who the precursors are, the next step is to use the surnames of the precursors comprising ‘machlup’, ‘drucker’, ‘bell’, ‘rubin’, ‘nonaka’, ‘takeuchi’, ‘von krogh’, ‘davenport’, ‘prusak’, ‘volpel’, ‘van dijk’, ‘castell’, ‘UN’, ‘UNESCO’ and ‘OECD” (altogether, fifteen names) to conduct a literature search to find who are the followers and critiques of these precursors. The search criterion for the publication period is from 1980 and 2020 (41 years), but the disciplines of the journals (e.g. management or production) are not set as a criterion for the literature search so as to obtain as many articles as possible. The starting year chosen is due to data availability as most search engines are not able to locate articles prior to 1980 (Choong, 2014 ). The literature search is based on a systematic review methodology as it is preferred to the traditional literature review because the former is particularly suited in a large literature survey for the gathering, evaluating and analysing of all the available articles relevant to a set of research questions (RQ) (Leseure et al., 2004 ; Kitchenham & Charters, 2007 ; Choong, 2014 ).

All literature searches were conducted using major journal databases such as ABI/Inform ProQuest, Emerald Full Text, Scopus and EBSCO. To be very certain that no relevant articles were left out from the literature search, a search was conducted on the Internet using Google Scholar for any publications pertaining to this research from 1980 onwards. While this procedure did not guarantee an exhaustive collection of all relevant knowledge economy articles, we believe that a large majority of relevant papers were found, and the resulting findings from this research suffice to provide a strong conclusion concerning the topic of the knowledge economy.

Content Analysis

Content analysis needs no introduction as it is a widely used method in social sciences and humanities to identify commonalities (common grounds) for studying and/or retrieving meaningful information from documents (Krippendorff, 2004 ; Jones, 2019 ) oriented to the study of ‘mute evidence’ of texts and artefacts (Hodder, 1994 , p. 155). Content analysis involves the selection of articles that have a high content of a research question, not people. But no matter how hard one tries, one just cannot analyse content in all possible ways, and this means it is impossible to be comprehensive. Hence, after the analysis, you will usually want to generalize those findings to a broader context—in other words, you are hoping that the issue you selected is a representative sample. Although content analysis is usually used to analyse written words, it is a quantitative method. The results of content analysis are numbers or counts, for example, how many authors use the term ‘knowledge economy’ as compared with those using ‘information economy’. The numbers and counting serve two purposes: to (1) remove much of the subjectivity from summaries and (2) to simplify the detection of trends. These help to aid human judgement in assigning relevance to the content.

Critical Analysis

Critical theory is well suited for this research as writing about the knowledge economy (which is considered a social science) is in many ways subjective where terms are used discriminately, and there is no cohesive body of thoughts (theory) in explaining what this form of economy is about. Critical theory is all about challenging what we have been told; it does enable us in developing a universally coherent study of the field through critical analysis and objective assessment of the content of articles.

In particular, we follow the Horkheimer’s ( 1982 ) approach in applying critical theory in structuring our paper. This is because Horkheimer wanted to distinguish critical theory as a radical, emancipatory form of social theory oriented toward critiquing and changing society as a whole in its historical specificity—it provides the descriptive and normative bases for social inquiry aimed at meeting three criteria: it must be explanatory, practical, and normative, all at the same time. Thus, Horkheimer’s approach is most consistent to the knowledge economy/society because it must explain why the knowledge economy cannot fit with current economic and social reality, identify the actors/phenomenon that bring about the change and provide both clear norms for criticism and achievable, practical goals for economic and social transformation.

The Precursors of the Knowledge Economy

Here, based on Rooney et al. ( 2005 ), and Godin ( 2010 ), we review and summarize the fundamentals of the KE divided into two subsections: (1) the first wave of studies on the knowledge economy and (2) the second wave of studies on the knowledge economy.

The First Wave of Studies on the Knowledge Economy

The first economist to write about the new economic direction of policymakers was the notable Austrian economist, Fritz Machlup (1902–1983). In The Production and Distribution of Knowledge in the United States , Machlup ( 1962 ) defined knowledge and prescribed the usefulness of knowledge in multi-facet perspective, especially on how it is used in production in creating the knowledge industry. In 1970, with Una Mansfield, they emphasized that education provides important impetus in creating a society that relies on information and knowledge in creating jobs and foster growth and the knowledge sector is increasingly growing in importance. In particular, Machlup’s books give rise to a new idea of learning and discourse that could impact an economy, now termed as the knowledge economy .

Peter Drucker (1909–2005), another Austrian economist, was quick to capitalize on Machlup’s idea when he wrote The Age of Discontinuity (Drucker, 1992a ) where the term knowledge workers was used for the first time. To Drucker, knowledge workers differ from manual workers as the former, mainly work with their heads, not hands, and produce ideas, knowledge and information. Drucker was describing these changes in the US economy during the 1930s when R&D departments were just blossoming in organizations. The emergence of knowledge workers causes changes in social, political and economic factors in the USA, resulted in the emergence of the KE.

The pace of the change in the US economy quickened in the late 1960s and early 1970s where there was a sudden influx of service workers in big towns and cities from rural farms—the most obvious measure of the transition from manufacturing to a service-based economy. To capture this change, Daniel Bell (1919–2011), a US sociologist and a neo-conservative, in The Coming of Post-Industrial Society (Bell, 1973 ), hypothesized that a new form of society, coined as the post-industrial society , will emerge in the USA and in other developed countries. This idea is ‘primarily’ about the change in US society brought about by changes in the economy, nature of work and politics from a nation producing goods to one based on a service economy. He asserted that the post-industrialized economy is not universal as it relates only to those countries that have successfully transformed from an agricultural economy to manufacturing economy. Later, Bell ( 1979 ) renamed this concept the Information Society ( IS ), for which he continued to discuss the significance of information and education in the creation of knowledge.

In 1977, Marc Uri Porat and Michael Rubin, both employees at the US Department of Commerce, wrote a Federal policy handbook that measured and estimated the size of the ‘information activity’ of the US economy, which is called the Information Economy ( IE ) . They found that the creation, handling and distribution of information were fast becoming a major economic activity for all nations of the world, be they rich or poor, developed or less developed. Porat and Rubin’s works have since been used in the USA, several countries and the OECD in describing the information economy.

The Second Wave of Studies on the Knowledge Economy

Nonaka and associates provided a new framework in the study of knowledge as (i) knowing and (ii) valued products produced by knowledge workers (Nonaka, 1991 , 1994 ; Nonaka & Takeuchi, 1995 ). Nonaka and Takeuchi ( 1995 ) conceive that human knowledge can be differentiated between explicit and tacit knowledge—the hallmark of their thesis. Explicit knowledge is ‘knowing that’ knowledge we can express and is contained in manuals and procedures; hence, it is coded and transferable, and tacit knowledge is scientifically articulated as ineffable or ‘knowing how’ (knowledge we cannot express) (Polanyi, 1966 ). Tacit knowledge is learned only by experience, and communicated only indirectly, through metaphor and analogy (Nonaka & Takeuchi, 1995 , p. 58–63). Later with von Krogh (von Krogh et al., 2000a ; Nonaka & von Krogh, 2009 ), they hypothesized that successful knowledge creation activities are supported by a SECI model , the acronym for Socialization, Externalization, Combination and Internalization. The works of Nonaka ( 1991 , 1994 ), Nonaka & Takeuchi ( 1995 ) and Von Krogh et al. ( 2000a , b ) are regarded as the precursors of Knowledge Management ( KM ).

Davenport and colleagues adopted part of Nonaka and Takeuchi’s conceptualization of human knowledge and indicated that by transferring their tacit knowledge to fellow workers, knowledge workers have a more vital role to play in the contribution of business strategy (Davenport & Prusak, 1998 ; Davenport & Völpel, 2001 ; Davenport, 2005 ). They argued that it is the tacit knowledge that is crucial to management as it is embedded in organizational routines, processes, practices, norms and innovations. As yet, a theory of knowledge management has not been reached (Pawlowski & Bick, 2012 , p. 96).

At about the same time, the Dutch information sociologist, Jan van Dijk, defined the Network Society ( NS ) as a ‘social formation with an infrastructure of social and media networks enabling its prime mode of organization at all levels (individual, group, organizational & societal)’ ( 2006 , p. 20). He observed that in the era of widespread use of microcomputers, the Internet and various IT devices link people and organizations together, and that when a society has attained these forms of informational and network structures, society is in a process of becoming a network society. Under a similar vein, Manuel Castells, the Spanish information sociologist, extends the information age trilogy to the NS and emphasizes the importance of networking (Castells, 1997 , 2010 ). He argued that in the information age, the increasing use of networks ‘… constitute the new social morphology of our societies, and the diffusion of networking logic substantially modifies the operation and outcomes in processes of production, experience, power, and culture’ (Castells, 2010 , p. 500).

To UNESCO, according to its General Sub-Director for Communication and Information, Abdul Waheed Khan, the concept of knowledge societies ( KS ) is preferable to that of the ‘information society’ because the former includes a dimension of social, cultural, economic, political and institutional transformation: collectively, a more pluralistic and developmental perspective that better captures the complexity and dynamism of the changes taking place (UNESCO, 2003 , p. 7 − 8). Thinking that the term and its expression are perfunctory and have not gone far enough, the OECD changed its inculcation theme by defining it as the knowledge-based economies ( KBE ). The OECD ( 1996a , p. 3) defined the KBE as ‘economies which are directly based on the production, distribution and use of knowledge and information’ that represents the emergence of a ‘super’ economy that is knowledge-based which brings in many benefits, and thus, developing countries should utilize knowledge and the creation and sharing of knowledge across countries in order to reap these benefits.

In more recent development, the European Bank for Reconstruction and Development (EBRD) advances the concept of the knowledge economy as a part of economic development, in which innovation and access to information drive productivity growth (EBRD, 2019 ). New trends, such as the Internet of Things (IoT) or digitalization, are examples of key elements of the transition towards the knowledge economy. To measure the discerned knowledge economy, the Bank has constructed the EBRD Knowledge Economy Index, spanning 46 economies (38 where the EBRD invests and eight comparators (members of the Organisation for Economic Co-operation and Development, OECD)) divided into four pillars of long-term knowledge-enhancing activities: (1) institutions for innovation, (2) skills for innovation, (3) innovation system and (4) ICT infrastructure. Among the EBRD regions, Estonia scores highest and Turkmenistan lowest.

In sum, the various terms used and the gist of the writings by the precursors of the knowledge economy/society are depicted in Table Table1 1 .

Terms/topics used by precursors of the knowledge economy/society

There are eight terms prescribing the knowledge economy/society or variation of such economy/society. For instance, Machlup was discussing about the knowledge industry while Drucker talked about the knowledge economy. Nonaka and Takeuchi and associates centre on knowledge, and knowledge creation within an organization, and when these activities are aggregated on a macro level, a kind of KE emerges. Van Dijk and Castells discussed about the network society. This multiplicity of terms is confusing as they can describe either the same or different things, and this inevitably spur scholars and critics (including us) to question the assumptions and rationale behind the work of the precursors. To us, we want to know whether the various terms are used in a casual manner or there is a serious discourse to refer to the knowledge economy. In our content analysis, we found that 91 out of the 121 articles (76%) in our References use the term ‘knowledge economy’ which confirms our earlier assertion that this term is most commonly used to describe this kind of the new economy.

Commentary of the Precursors of the Knowledge Economy

The commentators of Machlup’s thesis of an information and knowledge industry indicated that it lacks traditional economic theoretical foundation (Godin, 2006 , 2010 ) and subjective methodology in formulating models (Apte & Nath, 2004 ; Connell, 2007 ). At best, it only provides a crude retrospective approximation of modern economies (Ormerod, 1997 ), and there are scepticisms about data and his analysis (Godin, 2010 ). More specifically, Machlup’s definition of knowledge is ambiguous and his definition of the ‘knowledge industry’ was extremely wide, encompassing everything from the production of typewriters and filing cabinets to electronic, print advertising and mass media (Godin, 2008 , 2010 ). Arrow ( 1984 , p. 142–3) stated that Machlup’s definition of the knowledge/information economy runs contrary (flaw) to the economic ‘meaning of information is precisely a reduction in uncertainty’, which would exclude ‘information producing’ activity such as advertising, market research and most reports about the new economy. In our content and critical analyses, while we concur with the commentators that Machlup’s works lack theory (including poor definition of the knowledge and its industry), we acknowledge his meritorious work in the advancement of knowledge and its usefulness in production, and this sector is increasingly growing in importance. And this has proven right.

Drucker’s writings have been criticized for lacking academic and scholarly content where principles were far from obvious or still not defined (Parkinson et al., 1987 ; Edersheim, 2007 ), for being contradicting (Parkinson et al., 1987 ) and for being cryptic in his pronouncements as his works lack empirically testable propositions (Edersheim, 2007 ; Sapru, 2008 ). Drucker portrays knowledge workers as the preferred workers in a society which is akin to ‘brains over brawns’ in an economy where knowledge, knowledge management and skills dominate (Hadad, 2017 ). To Drucker, knowledge workers are the key to innovation and knowledge to an organization, and having them would enable organization to generate new products and inventions. In our analysis, although we do find Drucker’s writings to lack academic fervour, to be sometimes contradicting and to be apt in making arresting generalizations rather than to offering rigorous arguments in advancing a rationale of what actually is the KE, we do find meritorious aspects of his work, especially with respect to the notion that knowledge workers are the ones that give ideas and knowledge that initiate R&D activities in firms in boosting capability and the advancement of an economy.

Bell’s writings have been described as wide and to use imprecise terms (Waters, 1996 ). Gorz ( 1982 , p. 84) argued that the economic activities during the industrial era and Bell’s post-industrial economy era are basically the same. Robins and Webster ( 1999 , p. 80) stated that the information society, in fact, ‘can be, and has been—achieved on the basis of minimal technological support.’ Such assumptions are ideological in nature because they would fit with the view that we can do nothing about change and have to adapt to existing political realities (Webster, 2004a p. 267). Critiques (Gorz, 1982 ; Giddens, 1990 ; Waters, 1996 ; Robins & Webster, 1999 ; Mackay, 2001 ; Webster, 2002a , b ; Cornish, 2011 ) argue that the growth of information does not alter the attributes of the overall capitalist structure. Our content analysis indicates that the term post-industrial society originated from Alain Touraine (1925–) where many facets of his ideas such as the shaping of a ‘newer’ form of society through structural mechanisms (management, production, organization, distribution) are not much different from Bell’s indication that as wealth increases through incomes generated by workers, new demands arise for ‘luxury’; personal services such as hotels, restaurants and entertainment; and the demands for the government to provide for better health and education services. This, in our view, is a logical extension of economic activities from an industrial society to a post-industrial society, and thus, the terms post-industrial economy and post-industrial society are synonymous.

The studies of Porat and Rubin have been criticized for the lack theoretical building blocks (Miles, 1990 ; Engelbrecht, 1997 ; Wellenius, 1988 ; Apte & Nath, 2004 ), use of inappropriate measurement systems (Engelbrecht, 1997 ; Wellenius, 1988 ; Apte & Nath, 2004 ), lackluster categorization methodology (Apte & Nath, 2004 ), use of unadjusted statistics which may inflate the size of the information economy (Apte & Nath, 2004 ) and the reflecting of a mixture of information and service-business activities rather than knowledge (Arrow, 1984 ). Our content analysis confirms these critiques’ assertions that the assertion and reported numbers of Porat and Rubin lack measurement theory, and there was inadequate classification between information and non-information activities. Let us elaborate. First, the data reported by Porat and Rubin was obtained from government statistics where the various production and service activities are measured on the traditional value-added basis rather than on innovative or information-based basis. Due to this, actual information activities are not differentiated from economic activities. Second, due to inadequate classification between information and non-information activities, goods and jobs, we consider the actual size of the information economy as advocated by Porat and Rubin to be overstated. Third, we argue that even if we accept Porat and Rubin’s classification and measurement methodology, new information activities continually emerge with the advent of new technology, the application varies and the nature and scope of occupations have been continually changing making it imperative that the list of information activities, application and occupations be evaluated and updated regularly. Fourth, we notice that much of Porat and Rubin’s ideas are derived from earlier precursors (i.e. Machlup and Drucker) with little innovation of their own, and thus their works do not sit well in rigorous academic research.

The works of Nonaka ( 1991 , 1994 ) and Nonaka and Takeuchi ( 1995 ) have attracted numerous commentaries and relentless attacks for their (1) conception of knowledge and (2) knowledge creation (innovation) and knowledge management. First, critiques such as Jorna ( 1998 ), Wilson ( 2002 ), Hildreth and Kimble ( 2002 ) and Gourlay ( 2006 ) have criticized that Nonaka and his co-authors link tacit knowledge to an East-Asian phenomenological epistemology that is heavily influenced by Confucianism, Zen Buddhism and the collectivism culture of East Asia when putting forward their tacit and explicit knowledge arguments in advancing the concept of knowledge for commercialization. Second, Nonaka and Takeuchi’s SECI framework is subjected to the critiques that it is oversimplified in catering for knowledge management as the articulation of knowledge appeared to be possible even for tacit knowledge, and their statement that the sharing of knowledge can be achieved through a socialization process seems obnoxious (Jorna, 1998 ; Choo, 1998 ; Wilson, 2002 ; Hildreth & Kimble, 2002 ; Swan & Scarborough, 2002 ; Styhre, 2003 ; Kupers, 2005 ; Gourlay, 2006 ).

We consider the criticisms of Nonaka and Takeuchi on both counts to be misguided, myopic and unwarranted in many respects. First, basing on content and critical analysis, the critiques against Nonaka and Takeuchi of using non-Western epistemology in advancing an interesting new phenomenon of firms using knowledge in managing activities that can improve a country’s competitiveness are not based on constructive substantiation or hard evidence that there is any fallacy of East Asian philosophy as used in knowledge application. We also argue that the study of knowledge has been heavily undertaken in East Asia for thousands of years where Confucianism, Shintoism, Taoism and Buddhism play a great part in instilling what knowledge is about. We however, recognize that there are differences in the approach between East Asian and Western epistemology, but the outcome is likely to be the same. Based on the prevailing traditional Western epistemologies, knowledge has mainly been gained through observation and reasoning; however, in traditional Chinese thought, knowledge has been understood in a much broader sense (Rosker, 2014 ). More specifically, Western philosophy is about dialectics which contains thesis and antithesis and how they are resolved through synthesis, whereas in Eastern philosophy, a phenomenon such as KM is viewed as inherently paradoxical where the interrelatedness of the phenomenon in question is in terms of opposites that neither compromise nor repel each other but rather work in the dynamic combination of those dual entities and thereby build a composite whole (Chae & Bloodgood, 2006 ). They state that knowledge exists in the form of both tacit and explicit and, as such, social and technical, together building one composite whole—the Tao or the ‘ideal’ approach to KM—implying that in duality, unity is found (Chae & Bloodgood, 2006 , p. 7). As a consequent, these critiques fail to see and understand how Eastern countries such as China, Japan, South Korea and Taiwan (China) have successfully managed knowledge in pursuing innovations and inventions.

Second, in retrospect, Nonaka and Takeuchi had indicated that their approach in establishing a workable knowledge-based model is still ongoing and various aspects of work still remain to be done and called for more research to validate his model. Instead, these critiques merely argued that tacit knowledge is difficult to articulate, and hence it is quite impossible to transfer since ‘tacit’ means ‘hidden’, and that tacit knowledge is hidden knowledge. This testimony is nothing new as it was posited by Polanyi ( 1958 ), but these critiques failed to understand that in Nonaka & Takeuchi’s thesis, tacit knowledge alone does not suffice, that a socialization process is needed to transfer tacit to explicit knowledge in creating knowledge outcome. Notwithstanding these criticisms, the SECI framework has now attracted many prominent researchers such as von Krough, Davenport, Prusak and Volpel and they have contributed to the refinement of the framework.

Opponents of van Dijk’s network society consider that his form of society organized around global networks of capital, social, management and information through the multiplicity of interconnected tasks to be nothing more than a contemporary society (Fuchs, 2009 ). There is nothing theoretical about how a society can become a network society besides by stating propositions and facts that are currently in existence (Webster, 2002a , b , 2004 ). From the writings of critiques, we detected that there are two key issues of the network society as advocated by van Dijk. First, this kind of network society may only be achieved partially as it depends on high educational attainment and high educational use (see Di Maggio & Celeste, 2004 ) and is perceived in creating a digital divide (see Norris, 2001 ; Nakata, 2002 ; Rice & Haythornthwaite, 2006 ; Abdulla, 2007 ; Sharma et al., 2008 ; Allagui, 2009 ). To us, the reported increasing, widening and thickening of networks in nature and society by van Dijk indicate that networks have become the nervous system of society rather than one that helps to bridge people and society through the application of technology. Second, in view of the first point, even developed countries are not able to develop into a full-fledged network society as advocated by van Dijk.

Our analysis indicates that the content of the network economy as advocated by Castells is nothing more than a rephrasing of the term information economy or knowledge economy as his assertion has been made before by Bell, Porat, Rubin, Nonaka and Takeuchi, among others. This is nothing more than technological determinism (see Webster, 2002a , b , 2004 ; Garnham, 2004a ). Thus, in our view, networks and the network society are nothing new. If there is anything new, it can be perceived as the microelectronics-based, networking technologies that provide new capabilities to an old form of social organization that characterized the industrial society. That is, the network society is viewed as a social economy , and it has very little to do with factors of production or knowledge management.

We analyse the content of the description of the knowledge society advocated by the UN and UNESCO, and the knowledge-based economy by the OECD. According to Godin ( 2004 , 2006 ), the writings of the UN, UNESCO and the OECD lack academic rigor and instead they are meant to be rhetoric. Relatedly, Smith ( 2002 , p. 6) indicated that the various terms linked to an economy that is referred to as ‘ knowledge-intensive ’ or ‘ knowledge-based ’ are used in a superficial and uncritical way as there is no coherent definition, let alone a theoretical concept to explain their differences from one another. UNESCO led us to believe that lifelong learning can be achieved by uniting the community of scientists, researchers, engineers and technicians, research networks and firms involved in the process of research and production of high-tech goods and services, and so doing will integrate into international networks on production, distribution, use and protection of knowledge (Hadad, 2017 ). From our analysis, the term knowledge society is conveniently coined to tell us that we live in a ‘Shangri-La kind of society’. However, the statistical outcome tells another story. Although both bodies (which are under the same umbrella) use the same data and the same statistical technique, they produce different outcomes and these differences reflect different understandings of what the knowledge society is (see Oxley et al., 2007 , p. 17), suggesting that the characterization of the knowledge society is flawed.

The term knowledge-based economy is rhetorical (Godin, 2004 , p. 680, 2006 , p. 19), a metaphor ‘often used in a superficial and uncritical way’ (Smith, 2002 , p.5), and is a buzzword (Godin, 2004 , p. 688). To Godin ( 2004 , 2006 ), the moves by the OECD appear sinister as they led us in falsehood in believing that the new economy is already in existence or is a paradise we ought to aspire. To Sharma et al. ( 2008 , p. 152), the concept of a knowledge-based economy is nothing more than an information economy except that the former is broader as, in addition to technical knowledge, it also includes culture, social and managerial knowledge. The OECD, being a grouping of developed countries, is often a political tool for member governments in promoting familiar proposals around training, skills and infrastructure and sustainable growth coupled with better education and wider access to high-speed internet, and yet, the amount of public investment of those activities contemplated is comparatively low (O’Donovan, 2020 ).

Finally, we evaluate the content of EBRD. The priority areas for EBRD operations and activities are laid out in Article 1 of the institution’s founding constitution, which are (1) developing the financial sector through technical assistance to governments and bank officials; (2) supporting the creation of new financial institutions; (3) developing infrastructure, information technology, telecommunications and transportation, improving energy consumption and ensuring a healthy environment; (4) converting the military industry for civilian use; (5) general privatization; (6) restructuring existing industries; and (7) supporting small and medium-sized enterprises (Besley et al., 2020 ; Shield, 2020 ). These overlap considerably with the objectives of the IMF and World Bank. Many critiques and NGOs have criticized the EBRD on the lack of progress the Bank makes in its main mission, the ‘transition towards open, democratic and progressive market economies’; moreover, they consider some of its funded projects to be environmentally and socially harmful (Neslen, 2015 ; Bankwatch, 2017 ). Therefore, the EBRD aims at engaging policymakers and influencing policy debates rather than adopting policies actively provocatively towards a knowledge economy. Moreover, there is a general lack of economy or any theory and relatively little academic analysis of the EBRD, yet it is an institution in the vanguard of development strategies both in and increasingly well beyond Eastern Central Europe (ECE).

We summarize the key commentaries of the knowledge economy as advocated by the precursors of the field in Table Table2 2 .

Major criticisms of the knowledge (information, network, management) economy (industry, society) in relation to research implications

In our content analysis, we found that all of the 27 key commentators of the first wave of studies on the knowledge economy were critical of the views of the precursors. The works of Machlup, Drucker, Porat and Rubin, and Bell are closely related, but the definition of knowledge (information) by these precursors is so wide that it is anything but knowledge, and an appropriate statistical technique in classifying information and knowledge is wanting. For instance, when describing how knowledge workers use their heads, not hands, in producing goods and services, Drucker also mentioned that these workers should also like to please the boss. And when he wrote about social, political and economic changes of the ‘postcapitalist’ era of the ‘First World Nations’, meaning the USA, ordinary citizens become virtually owners of the great American enterprises, i.e. being owners of the capital in overcoming capitalism—an arresting statement that has little to do with the knowledge economy. Nevertheless, in spite of certain inaccuracies, we regard Drucker’s works of merit where he did make a number of accurate forecasts, as well as astute perceptions of 1993 that remain true today: (1) the developed countries will be inundated by a human flood of Third World immigrants; (2) in knowledge work and in most service work, the machine (if any) is a servant to the worker; (3) a severe problem is the diversion of the scarcest resources—trained engineers and scientists to economically unproductive defence work—the malefactor being the USA where over 70% of all money spent on R&D is spent on defence work (affirm by Phillips et al., 2017 ); and (4) knowledge, knowledge workers and technological progress determine quality of products—the case of Japan being the first non-Western nation in modern times to become a great economic power. According to Drucker ( 1993 ), the winners in this new economy will be those who master knowledge about knowledge.

Of the 33 key commentaries of the second wave, only the works of Nonaka and Takeuchi received some favourable commentaries, and in fact, their notable work on the SECI framework has been embraced by notable researchers. We consider that Nonaka and Takeuchi had initiated a difficult paradigm of articulating tacit knowledge in the conception of knowledge in knowledge creation (management) where, if properly transferred (applied) among knowledge workers, it would lead to the sustaining of competitive advantage of firms and the collective effort of firms would enable the country to prosper in the future. On the contrary, the network society expounded by van Dijk and Castells is nothing more than a mere extension of the information economy. Third, we rule out the writings of the UN, UNESCO, OECD and EBRD to be scholarly and worthy of research consideration as they not only lack academic rigor but also meant to be rhetoric or myths. Moreover, we see they serve certain government policies and political agendas that are geared towards the West, particularly the OECD and the EBRD.

We feel that one particular crucial aspect both the first and second waves of studies of the knowledge economy have missed out is health matter—to build a strong and progressive society/economy, there must be in place a good health system to take care of not only knowledge workers but also the population in general. Investment in health is not only desirable; it is an essential priority for most societies. This is especially so in view of the current Covid-19 pandemic. Healthcare performance is strongly dependent not only on the economy but also on the health systems themselves: all these are essential to a healthy society (Eissa, 2020 ).

Theoretical Consideration in Advancing the Research on the Knowledge Economy

Seeking common ground in the furtherance of the research of the knowledge economy.

Despite the various issues surrounding the works of the knowledge economy, there are commonalities of thoughts and propositions among the precursors, suggesting that there is a common ground for us to establish some theoretical foundations of the field. First, we need to rationalize whether the terms economy and society have any significant difference in meaning. The relationship between economy and society and how it is determined is a matter of theoretical debate. The classical economists (including Emile Durkheim, 1858–1917; and Max Weber, 1864–1920) have articulated a new form of the economy (at their time) in a way where people envisage is probable. Durkheim viewed the economy as one of the numbers of social institutions that make up a society, and Weber viewed the economy in part as an extension of social values and religious belief. Therefore, the economy and society are inextricably linked.

Second, in our critical analysis, we realize that the differences in terms used by the precursors of the knowledge economy are partly due to the time factor because the stages of the economy change with time. In the industrial economy , an old economic dictum of Adam Smith (1723–1790) existed from the eighteenth century in which agricultural products and mass-produced manufactured goods were the key components of the economy, and the majority of workers were farmers, clerks or machinists. The post-industrial economy ( information society ) is the period from the late 1960s where mass production from the developed countries shifted to developing countries, and the mainstay of economic activities of the developed countries is services supported by technology (mainly computerization) and changes of society (social values).

Nonaka, Takeuchi, Davenport, Krogh, Prusak and Volpel focus on knowledge on the organizational level, i.e. knowledge management, which when aggregated among firms would contribute to the notion of the knowledge economy. This period is around mid-1990s. Despite critiques claiming that there is no difference between an information economy and a knowledge economy, we argue that knowledge is more than information because knowledge is being creative and innovative (i.e. it uses information to make things different from others), thus giving us a competitive advantage. That is, the use of knowledge is increasingly of a greater magnitude and importance in a knowledge economy era (Houghton & Sheehan, 2000 ; Giddens, 2001 ; Powell & Snellman, 2004 ; Roberts, 2010 ; Garza-Rodriguez et al., 2020 ). In a study conducted for Mexico, the results show that the impact of human capital on economic growth is almost three times greater than that of physical capital (Garza-Rodriguez et al., 2020 ). Notice that none of the precursors emphasize on production and consumption. The various stages of economic (societal) development are depicted in Fig. Fig.1 1 .

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Stages of economic and social development

Figure Figure1 1 shows the evolution of the economies (societies) from the pre-industrial to industrial stage and finally to the post-industrial stage. In the post-industrial stage, three variations of economies/societies are envisaged. The information economy (society) is the progression from the industrial economy (society). While there is a progression from the information to knowledge economy, the path of the network society is difficult to envisage as it appears that it is in a stage of a continuum but where it is heading is unknown. It does not mean that once a country/society has attained a high amount of ICT expenditure or economic development, it can be considered a knowledge economy. Take the case of Hong Kong, HK (China), many of its residents and HK specialists regard HK as a knowledge economy, but in actual fact HK is predominately a service economy rather than a capital-intensive technologically advanced economy. Zhu and Chou ( 2020 ) indicate that although HK has made improvements in knowledge-induced productivity from 1991 to 2011, it still has some ways to go to meet the requirements of a knowledge economy. There is still an economic gap between occupations that are knowledge-intensive and knowledge-non-intensive, and there are overeducated employees who find themselves unemployable. Unless these deficiencies are addressed, transforming HK’s economy from a service economy to a knowledge economy appears unattainable.

What is more important is that, from the study of the various precursors’ works, we are now getting nearer in understanding this new kind of economy, i.e. by finding a common ground to show how a society becomes a knowledge economy:

We estimate the starting point of the knowledge economy to be mid-1990s.
There is no difference whether the new economy is viewed as an economy or society.
The knowledge economy is not really an economy based solely on production and consumption by exploiting labour and capital, but it is based more on a social value and structure.
Extensive use of technology and the ability for us to exploit them for commercial reasons and for personal and organizational enjoyment (i.e. sociability or societal benefits).
Extensive use of innovation to commercialize knowledge (knowledge creation, knowledge management)

Theoretical Framework of the Knowledge Economy and Its Usefulness for Future Research

The discussion from the previous subsection provides us with the theoretical foundation needed to define a knowledge economy (see Fig. Fig.2 2 ).

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Suggested key aspects of the knowledge economy that may be considered as the common ground in researching the field

In Fig. Fig.2, 2 , the knowledge economy consists of sociability, technology, knowledge and innovation, and knowledge products.

The authors/articles who have contributed to our foundation of the Knowledge Economy/Society are presented in Table Table3 3 :

Authors (articles) who contributed to our foundation of Knowledge Economy/Society

Our discussion of the sub-header will be made in conjunction with Fig. Fig.2 2 and Table Table3 3 .

Social Economy

In our critical analysis, we found that the knowledge economy is not an all-encompassing economy as it cannot address (all) the various aspects of technology, innovation and organizational and people’s needs. Moreover, the knowledge economy lacks a theory. In our search for solutions, we found that this kind of economy that can offer us with good explanations of the radical change in the ways people think and expect in a society, and how organizations (institutions) are utilizing innovation by tapping the power of technology in creating knowledge products in the furtherance of the economic well-being of a society (country), is viewed in terms of the social economy .

We found that the idea of the social economy has attracted researchers, governments and non-government agencies especially in continental Europe and Canada (Gueslin, 1987 ; Demoustier, 2004 ; Moulaert & Ailenei, 2005 ). According to these writers, the social economy apparently arises through the progression of time, and its theoretical foundation can only be analysed by combining a ‘history of practice’ with ‘a history of thought perspective’. The nineteenth century ought to be taken as the formative era of the modern social economy as it was characterized by an outburst of ideas, concepts and experiences; cooperative, associative and social awareness; institutional and utopian initiatives and the emergence of social, economic and political liberal philosophies in reaction to the social brutalities (poverty and exploitation), and political indifference (lack of social welfare) of the Industrial Revolution. This assertion is also shared by Le ´vesque et al. (2001; cited in Moulaert & Ailenei, 2005 ).

These writers also asserted that the theoretical arguments tend to be recurrent, and that the social economy is not an economic system on its own as there are diverse views of the foundation of this kind of economy. The diverse views may create a magnitude of possible theoretical foundations of what constitutes a knowledge economy. Because of these, it is exceedingly difficult, and probably not scientifically useful, to reconcile the wide world of initiatives and activities connected to the social economy in a ‘one for all’ definition (Moulaert & Ailenei, 2005 ).

In our content and critical analyses, we find that one social thesis that takes into consideration personal needs is the one that has been put forward by the international philosopher, author, poet, composer and linguist, P.R. Sarker (1921–1990). A series of lectures were published in 1959 as Idea and Ideology , where Sarkar described a socio-economic theory which he called Progressive Utilization Theory (PROUT) (Sarkar, 1959 ). The PROUT is based on progress, philosophy of life, spiritual belief and utilization of the qualities of practical education (as against academic education), morality and socio-economic consciousness in an economy in order to provide a good standard of living to all people and to see that economic power is not concentrated in the hands of a few.

In some ways, PROUT philosophy is akin to Drucker’s ( 1993 ) Post-Capitalist Society , Bell’s ( 1973 ) Post-Industrial Society or van Dijk’s ( 1999 ) Network Society in which developed societies that have exploited IT successfully will be able to transform from an agricultural economy to knowledge economy—towards a utilization society where there is a good standard of living to all people and to see that economic power is not concentrated in the hands of a few. Bell asserted that his thesis is about the attainment of an industrialized society and is not universal as it relates only to the USA while Drucker’s thesis applies only to developed countries such as the USA, Western Europe and Japan, and van Dijk considers that all countries are capable of attaining the network society status if they have successfully utilized IT. Except for van Dijk, the various KE writers are rather biased as they see that only the USA, Western Europe and Japan can become advanced economies. Little did they realise that South Korea, Taiwan (China) and soon China would become advanced technological countries.

It is true that the USA, Western Europe and Japan are wealthier countries than some of the developing countries, and so they can invest in a variety of things notably in health care. For instance, the USA devotes more of its national income to healthcare relative to other OECD countries where the OECD averaged about 8.7% of annual GDP; healthcare spending in the USA, however, stood at 17% while the rest of OECD countries range between 9.0% (Spain) to 12.1% (Switzerland) at 2019 (World Bank, 2020 ). This compares with 5.08% (Sub-Saharan countries), 7.96% (Latin American & Caribbean countries), 3.48% (South Asian countries), and 6.67% (East Asian & Pacific countries) (World Bank, 2020 ). Higher healthcare spending can be beneficial if it results in better health outcomes. But looking at the statistics, surely the USA and OECD countries would have spent much more on health and the outcomes ought to outperform developing countries. However, this is not the case. In view of the Covid-19 pandemic, the USA and developed countries fare worse than some developing countries notably East Asian countries like China, Taiwan (China), Vietnam and Thailand. High administration costs including salaries of medical professionals and high medicine costs are among the reasons cited why the USA and OECD countries have higher healthcare expenses in relation to developing countries (Peterson Foundation, 2020 ). Moreover, pandemic preparedness and response capacity of these countries are less efficient compared with the East Asian countries. Pandemic preparedness is not a new concept as several pandemics have occurred before. For instance, China and Vietnam can plan, mobilize and utilize resources and deploy emergency assets in quick time and they can treat and monitor patients on a sustainability basis. A healthcare system with high costs, inadequate preparedness and response and poor outcomes undermines our economy and threatens our long-term fiscal and economic well-being. Indeed, the recent pandemic has given a basic message: investments in health and the design of health financing policies should be addressed in terms of the interaction between health and the economy (Eissa, 2020 ). Just as growth, income, wealth, investment and employment are a function of the performance and quality of the economic system, its regulatory frameworks, trade and political policies, social capital and labour markets, etc., so health conditions (mortality, morbidity, disability, ability to confront pandemic) depend not just on standards of living but on the actual performance of health systems themselves.

Our analyses also reveal other useful findings. In an extensive study of the social economy of Canada, and in particular, Quebec, Ninacs and Toye ( 2002 ) articulated that the social economy is not an entirely new type of economy, consistent to the contention of Gueslin ( 1987 ), Demoustier ( 2004 ) and Moulaert and Ailenei ( 2005 ). Instead, Ninacs & Toye argued that the ‘newness’ related to the evolution of the social economy over the past 35 years or so is due to the presence of new types of people who become promoters or members, new stakeholders, new fields of activity, new organizational forms and new internal and external dynamics. Although many aspects of Ninacs and Toye’s ( 2002 ) arguments relate to knowledge and innovation, the indicated timeframe of the social economy evolution, which was supposed to arise in 1965, was a bit far-fetched from our posited period (mid-1990) of the formative years of the knowledge economy.

We found Brown’s ( 2008 ) work on the social economy relevant. Drawing on a wide range of writings on the social economy, Brown ( 2008 ) finds it useful to think of the theoretical foundation of the social economy in the following ways: (1) it is a branch of the economy that is concerned with the relationship between social behaviour, people well-being and economies, and (2) it is not an unorthodox school of economies (e.g. classical or neo-classical theories) and often takes into account subject matters outside the focus of orthodox economic theories. Thus, the social economy encourages people and society to utilize resources to satisfy human and community needs, including humanity, changes of educational and economic development, promotion of thinking and innovation in which human and society can collectively earn their minimum necessities through appropriate work in producing knowledge products. This economic system can be regarded as the organizational arrangement and process through which a society makes its production and consumption decisions which society deems desirable, like efficiency, growth, liberty and equality, consistent with what Conklin ( 1991 ) had advocated earlier.

In a similar vein, Nasioulasa and Marisb ( 2011 ) indicated that the social economy enhances the promotions of social cohesion, innovative entrepreneurship and employment with the help of digital and network technologies, thus sustaining an economy with a rising social need and the demand for uninhibited access to digital content throughout the cyberspace. This invariably is mainly orientated towards the use of computer technology for the creation of knowledge products. Knowledge products require innovation, and the importance of innovation is even more important in recent years as many markets have become mature, and many mature companies have failed to survive as they fail to capitalize on the exploitation of technology to innovate their products (see Lynch & Jin, 2016 ).

Therefore, in our view, we rule out PROUT to have much relevance to the knowledge economy as the first is too political with a heavy emphasis on the equilibrium of social needs and social and spiritual well-being. We consider that the knowledge economy is a branch of the social economy (as in Brown, 2008 ) (see also in Gueslin, 1987 ; Demoustier, 2004 ; Moulaert & Ailenei, 2005 ; Nasioulasa & Marisb, 2011 ) that is tied to social innovation, reliance on knowledge and technology and use of new types of production in churning out knowledge products or exploiting new markets that bring it into the realm of economic innovation. This is brought about by social entrepreneurs (innovators) working in social organizations where these entrepreneurs combine creativity with pragmatic skills in pioneering new solutions to social problems and in doing so change the patterns of society. Thus, the knowledge economy is a social change theory, much akin to Lewin’s contemporary social organization and management change model (see Huarng & Mas-Tur, 2016 ).

Technology (ICT)

In our content and critical analyses, this new kind of economy is made possible through the explosion and exploitation of technology, in particular ICT which begins from the mid-1990s, largely due to the emergence of the dot.com’s. From our review of the studies of the precursors of the knowledge economy, technology drives two key dynamics: (1) the improving functionality and commercialization of products and services and (2) the reducing unit cost of supplying them. Industry-wide progress clearly gives rise to products that would not have been possible just a few years earlier, and at increasingly affordable prices. Adapting to technological progress is important because the knowledge economy is a continuously adapting field between the public and private sectors, individuals and organizations in which there exists a rising social need and demand for uninhibited access to ICT especially semantic web and digital content throughout the cyberspace. The development of technology allows organizations and the public in benefitting not only from participating to the availability of many new commercial software but, more importantly, from sharing data and their interpretation of information (Robinson & Bauer, 2011 ). This includes innovative modalities among content, user and transit such as peer-to-peer networks that have substantially enhanced digital content delivery (Nasioulasa & Marisb, 2011 ). In Australia, the introduction of the national curriculum in 2013 places high emphasis on the integration of digital technologies throughout key learning areas. There are pedagogical justifications for using ICT across the key learning areas as young children must be made exposed to ICT literacy as it is clear that children learn to use technological devices very quickly (ICTE, 2020 ).

We found that Drucker’s integrative thinking illuminates the special obligations imposed by technology on modern business organizations. From witnessing the early development of technology in industry, Drucker noticed that in organizations, management and society there has been a massive overall change in the nature of technological work during our century—a change in its structure, costs, methods and conceptual underpinnings.

We can connect that the rise of the information society will see the emergence of a network society in which information, computer and technology will enable the formation of networks among users. Essentially, the network society, as Van Dijk sees it, can explain a new type of society where social relations are organised within mediative technologies that form a communication network rather than networks typified by face-to-face social relations. The network is developed in a society that allows for a great deal of information to be processed, exchanged and disseminated to help improve information and communication technologies—that is, it facilitates the globalization of IT.

The technological capability must be harnessed at both the micro and macro levels (Berners-Lee, 2010 ). For example, firms use serious games (i.e. video games used in a professional context) to make training more exciting and immersive as they take players into virtual worlds where they learn and experience real-world scenarios in a funny and entertaining way (Allal-Chérif & Makhlouf, 2016 ). On the macro level, we argue that technological innovation and ICT represent a way for knowledge economic countries to foster economic development, exploit technology (ICT), improve levels of education and training and address social (societal) issues within a knowledge economy, a view similar to OECD ( 2015 ).

Technology prowess does not stem from high ICT spending. High ICT spending by a country may have poor IT utilization, as seen in the case of high health spending which may have low health outcome. Following years of growth, ICT spending will remain relatively flat in 2020 due to the COVID-19 pandemic, and in the next 5 years, all growth in traditional tech spending will be driven by just four platforms: cloud, mobile, social and big data/analytics (ZDNet, 2020 ). For instance, Singapore has been spending heavily on ICT for the past years and the government plans to increase its ICT spend by 30% in its fiscal 2020 (ZDNet, 2020 ). However, Singapore is not considered a knowledge economy because most of its technology comes from abroad. Wang ( 2018 ) found that over 70% of Singapore’s private sector R&D expenditure and the bulk of industry patents came from foreign multi-nationals, and there is evidence that innovation is government ‘push’. Moreover, she is ill-equipped to handle and maintain technology as seen on 14 October 2020, when a damaged power cable led to a series of events that caused a major subway (MRT) disruption affecting three train lines —an incident which transport experts said could have been avoided (Straits Times, 2020 Dec 16).

Knowledge and Innovation (Knowledge Creation)

We argue that the rise of the interest in the knowledge economy has meant that economists have been challenged to look beyond labour and capital as the central factors of production. More recently, Aghion et al. ( 2004 ) have indicated that innovation (knowledge creation) in inducing competitiveness among organizations is considered a third important factor of production. In today’s complex, competitive and turbulent environment, the need for innovation in products and processes is widely recognized and organizations are required to apply new technologies and to innovate timely in anticipation of changes in the marketplace rather than as a reaction to business decline (Rahimi, 2017 ). Although knowledge and innovation have been widely discussed by the precursors of the second wave of studies on the knowledge economy, there are gaps in their articulation, viz. on how knowledge and innovation can help in transforming an economy to one that is based on knowledge. We will now explain this.

We consider thatknowledge is a valuable input that drives innovation, and the output is knowledge product. Knowledge products will be discussed in the next sub-section. In between input and output is knowledge management . Nonaka ( 1991 ) described tacit knowledge as the fuel for innovation but was concerned that many managers failed to understand how knowledge could be leveraged. Organizations are more like living organisms than machines, he argued, and most viewed knowledge as a static input to the corporate machine. Nonaka advocated a view of knowledge as a dynamic mechanism—renewable and changing—and that knowledge workers were the agents for that change. Nonaka and Takeuchi then forwarded the argument that creating knowledge will become the key to sustaining a competitive advantage for organizations in the future. As the competitive environment and customer preferences changes constantly, knowledge perishes quickly and therefore managers and knowledge workers must rejuvenate it. In addition, Nonaka and Takeuchi show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call ‘middle-up-down’ in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline workers. Thus, knowledge-creating companies, Nonaka and Takeuchi believed, should be focused primarily on the task of innovation.

Innovation can be attributed to various sources, and innovative processes usually involve identifying customer needs, macro and micro trends, developing competencies and finding financial support in order to sell the innovation—a strong motivation for the commercialization, or value creation of knowledge. Businesses must engage customers to develop products that sell. Interestingly, more recently, the casual claim between customers and innovations have been reversed in which customers are the ones that are important in the development of new innovations for commercial purposes (see Lettl et al., 2006 ; Desouza et al., 2008 ; Oberg, 2010 ). The innovation process ends upon the successful transformation of knowledge to innovation, resulting in the creation of value enhancement intellectual capital (IC), or intangible assets (IA) as called in accounting, which can generate future benefits to organizations (see Choong, 2008 ; Lages, 2016 ).

Knowledge Products (Commercialization and for Personal Enjoyment)

So what actually are knowledge products ? The literature indicates a wide range of activities that can constitute knowledge products such as skills, knowledge, know-how, work-related experience, competencies, education, creativity, brand, trademarks, intellectual property (IP), copyrights, trade secrets, work procedures and software, among others. We found that these products are no different from the products of the old economy. There is no clear definition. Drucker attempted to describe knowledge product in a protracted manner. Knowledge workers put knowledge to work and that information is not knowledge. Information must be applied to specific work, and there must have improved performance for it to be classified as knowledge. Drucker emphasized that only human beings with their brains and/or the skill of their hands can convert information into knowledge. The outcome of which the products so developed are new—i.e., newness determines a product to be knowledge product.

As Nonaka and Takeuchi pointed out, ‘understanding how organisations create new products … is important. A more fundamental need is to understand how organisations create new knowledge that makes such creations possible.’ Given in today’s competitive market, technological changes, customer demands and revolutionary technologies combine to place pressure on organizations to constantly innovate and provide cutting-edge outcomes. Nonaka, Takeuchi, Davenport and Völpel have indicated that the key is innovation and developing innovative capabilities such as architectural innovation capability enhances firms’ ability to respond to market demands by producing innovative products and services and can has a profound impact on their performance.

For us, knowledge products differ from other kinds of products in that their relevant and useful aspects reside primarily in the content that can be extracted from them, and as such, any physical manifestation thereof is usually at best a carrier medium . It is about talented people, either on their own or through organizations, on how they use their knowledge to innovate ideas to produce knowledge products. Also, they must have appeal so that the products can be commercialized. Thus, the creation of knowledge products creates social benefits to people, organizations and society. From these explanations, we rule out knowledge work (jobs), such as skills, know-how, work-related experience, competencies, education nad work procedures, to be knowledge products.

From the literature and our analysis, knowledge products are indeed difficult to define. Hence, we use an illustration to explain what exactly knowledge product is. Take the case of a mystery killer storybook written by an author. The book is a physical item, not a knowledge product, and the knowledge product is the intriguing story of the book, which is the research, innovative and artistic outcome arising from the tacit and explicit knowledge of the author, which are required to make it different from another story. Similarly, let us take a look at the music, dance and song of Billie Jean by Michael Jackson. The CD is a physical item, not a knowledge product, and the music, dance, expression and choreography, collectively, are the actual knowledge product. From these two examples, we explain that the physical element and the intellectual element must be differentiated or else there is no clear boundary between a tangible and intangible item. It can be seen that an intangible item cannot exist on its own right. In fact, both tangible and intangible items are complimentary of each other in the form of a knowledge product. Blended together, they create values and bring competitive advantage to the developers (talented people) and organizations. More importantly, the creation of knowledge products necessitates innovation and knowledge sharing, and the commercial outcome is benefits to people and organizations that use them, i.e. a social-economic perspective. More specifically, Muriel and Serrat ( 2009 ) and Rooney et al. ( 2012 ) have described knowledge products as the outcome of the production of knowledge, much of which represents intellectual capital.

There is no data for knowledge products by country. The closest data we obtained is data for high-technology exports in current prices published by Knoema which compiles data for the world and country level from sources such as the World Bank, IMF and OECD. Knoema defines high-technology products as products with high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments and electrical machinery. In 2019, Knoema ranks HK (China) to be the largest exporter of high-tech products, follow by Germany and the USA. Mexico is placed seventh and Belgium is placed eight. China, which is recognized as a factory for high-tech products, is not represented in the list which consists of 43 countries. Comoros, a country consisting of several tiny islands with less than a million people off the coast of Mozambique (East Africa), is ranked 43rd in terms of largest export of high-tech products. Ironically, HK (China) and to a lesser extend Mexico and Belgium are not really into manufacture of knowledge products. Surely, Comoros is not into high-tech knowledge products. Therefore, reliance on published statistics is misleading as seen in the case of health care and technology prowess.

The framework of the knowledge economy and the statistical assessment methodology proxied by the four proxies are as shown in Table Table4 4 .

The framework of the knowledge economy and the statistical assessment methodology proxied by the four proxies

‘Apparent’ Knowledge Economy Countries

This is the ultimate work with respect to this research, as there is no precedence or methodology concerning how countries can be construed to be knowledge economies. What is important is to establish the statistical assessment methodology for the level of knowledge economy against other type of economies (Rim et al . , 2019 ). They hypothesize the indicators come from two sources: (1) economic growth and the expressions of knowledge-based economy and (2) the statistical assessment methodology for the level of knowledge-based economy based on the first part. From this, we articulate one approach in which proxies (indicators) can be used to measure the four knowledge economy criteria we have thoroughly expounded earlier. They are (1) social economy; (2) technological strength; (3) knowledge & innovation; and (4) knowledge products.

We make use of per capita GDP (nominal) ranking of selected countries in ascertaining how well they relate to these four criteria (see Table Table4). 4 ). Some readers may argue on why we did not use GDP based on a PPP basis. We find that the PPP basis consists of too many consistencies as (1) the purchasing power is subjective; (2) the metrics used were not thorough researched; and (3) the purchasing power tends to give a high value to certain countries. We based our analysis on the selected 18 countries because it is beyond the scope of this paper to list every possible country in ascertaining whether or not they satisfy the criteria of the knowledge economy. We proxied social economy with social progress index produced by Social Progress Imperative, USA. Social progress is defined as ‘the capacity of a society to meet the basic human needs of its citizens, establish the building blocks that allow citizens and communities to enhance and sustain the quality of their lives, and create the conditions for all individuals to reach their full potential.’ (Institute for Strategy and Competitiveness, 2020 ) Technology (ICT) is proxied by the Technology Strength Index which consists of four integrated metrics, three of which serve as standard measures of the availability and prevalence of technology: (1) internet users as a proportion of the population; (2) smartphone users as a percentage of the population; and (3) LTE users as a percentage of the population. The fourth metric we used is a Digital Competitiveness score developed by the IMD World Competitiveness Center. Their competitiveness score focuses on technological knowledge, readiness for developing new technologies, and the ability to exploit and build on new innovations (Global Finance, 2020 ). Knowledge and Innovation are proxied by the Innovation Index, published by the World Intellectual Property Organization (WIPO, 2020 ) and as data is not available for Taiwan (China), we use Innovation Index taken from Bloomberg, NY, USA. It ranks 60 countries in terms of its ability to innovate its products and services (Bloomberg, 2020 ). Knowledge products are products created by knowledge or intellectual capital, and they are at the stage ready to be commercialized for personal enjoyment. They are proxied by two indexes: Patents granted and Trademarks in force. The indexes are published by the World Intellectual Property Organization (WIPO, 2020 ), Geneva, Switzerland. These indexes are by no means the best, but they are readily available. For instance, Tech-strength Index and the Innovation Index ranked certain countries far too high and certain countries too low. For instance, for Tech-Strength Index, Singapore is ranked eight, way ahead of Germany and South Korea and for Innovation Index, and UAE is ranked eight against powerhouse Japan and Taiwan (China).

We agree that other approaches or methodologies and other proxies may be used in ascertaining a knowledge economy, and this is an interesting avenue to explore. The selected countries illustrating knowledge economies are tabulated used for in Table Table5 5 .

The selected countries and their associated indexes that proxied the elements of knowledge economy

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GDP Per capital Nominal 2020 est. International Monetary Fund, Washington DC. Social Progress Index, 2020, Social Progress Imperative, US. Tech Strength, 2020, Global Finance, NY. World Global Innovation Index 2020, World Intellectual Property Organization (WIPO), Geneva, Switzerland. *Ranked by Bloomberg, 2020. World Patent Grants, 2019, Statista, NY, World Intellectual Property Organization (WIPO), Geneva, Switzerland. World trademarks in force, 2019, World Intellectual Property Organization (WIPO), Geneva, Switzerland

Although Qatar has the high per-capita GDP, it does not meet many of the requirements of a social economy (fail to satisfy most of the four elements) and so we exclude it for our analysis. We also exclude some other high-income countries/jurisdictions such as Macau (China), Ireland, Brunei, Kuwait, Bahamas, Malta and San Marino because of its small population. While Hong Kong (China), Singapore, Ireland and Iceland have high income and sufficiently high human capital, they lack knowledge and innovation to generate their own knowledge products, and hence they are not selected for consideration of knowledge economies. Oman, Spain, New Zealand, Poland and Hungary may have fairly high technology usage, but most of their knowledge products are imported. Russia and China are not high-income countries but are included for comparison purposes because of their strength in generating knowledge products. Although the USA has high GDP and certain good knowledge economy rating, it has many social problems such as income disparity between cities, high racialism, high crime rates, poor health care and unequal usage of IT, and hence, it does not meet the knowledge economy criteria. Also excluded are Saudi Arabia and Bahrain (poor social progress, poor innovation and poor IP data). So the apparent knowledge economy countries appear to relate to Luxembourg, Australia, the Netherlands, Sweden, Finland, Germany, Canada, Israel, France, UK, Japan, South Korea, Taiwan (China) and Lithuania. But the inability to control the Covid-19 pandemic has dented some of these countries from being an apparent knowledge economy. Of the countries selected, only China managed to have the pandemic under control but its moderate GDP and low social progress have dampened her to be a candidate of apparent knowledge economy. Therefore, it appears that only Luxembourg, Australia, Finland, South Korea, Taiwan (China) and Lithuania satisfy the knowledge economy criteria.

Conclusions and Discussion

Despite nearly 58 years since the term knowledge economy first appears, we are no nearer in understanding this new kind of economy. This lack of understanding is exacerbated as there is no universal acceptance of the definition of knowledge , knowledge creation, knowledge economy , knowledge society , service economy , network society , etc., and various associated terms such as knowledge, knowledge creation and socialization. Thus, as a result, significant doubts are caused concerns as to whether the ‘modern economies’ are, indeed, ‘knowledge economies’. It does not follow that only those countries transformed to servicing industries and to creating ‘knowledge’ in so-called knowledge work (white-collared jobs) will thrive to be transformed into knowledge economies. More critically, the current economic meltdown of these so-called knowledge economies is testimonies of false assertion by the precursors of the field. Such inadequacy occurs in view of the fact that the field has no shortage of writers and critics that advance or reject various avenues of the knowledge economy.

In this study, we have made a thorough examination of what the precursors have written about the knowledge economy and what the commenters have argued to put forward what a knowledge economy ought to be. The notion of the knowledge economy must be viewed from some phenomena that have transformed the contemporary economy, but in our content and critical analyses, none of the precursors has advocated an economic system that deals with the fundamentals of the factors of production and how these factors influence society. We, however, found that the works of Nonaka and Takeuchi received some favourable commentaries and we consider that they had initiated a difficult paradigm of articulating tacit knowledge in the conception of knowledge in knowledge creation (management) which if properly transferred (applied) among knowledge workers would lead to the sustaining of competitive advantage of firms and the collective effort of firms would enable the country to prosper in the future.

This study finds that the study of the knowledge economy is largely speculative where each precursor prescribes what knowledge, information or network ought to be, and suggests a particular economic system (society) that the precursor opines to be appropriate to be used in a particular time period. As such, the field has not been developed into a coherent study of social-economic knowledge, innovation thoughts and technology and knowledge products. Nevertheless, we found the field is interesting and has research usefulness. We achieve three major findings and hence offer these suggestions.

We do not need the multiplicity of terms to describe one thing (subject matter); all we need is to use the caption ‘knowledge economy’ in describing the contents of this new form of economy—i.e. by adopting the path of science, we define the field by its theory, not term. We see the term ‘knowledge economy’ and ‘knowledge society’ to be synonymous as an economy that utilizes knowledge to transform work into knowledge products progresses to become a knowledge economy. Likewise, knowledge workers who have successfully developed knowledge products would like to have personal enjoyment.
Using the notion of common ground, we articulate that the theoretical foundation of the knowledge economy is (1) a branch of social economy where the starting point is the mid-1990s and (2) not really an economy based solely on production and consumption by exploiting labour and capital, but it is based more on social values, technology and knowledge and innovation to commercialize knowledge products.
Using a novel methodology based on proxies, we found that our defined knowledge economy countries are those that satisfy the four criteria: sociability, high-technology adoption, possession of knowledge and innovation to produce knowledge products rather than merely having a high income or apparent high level of social progress, tech strength or innovation. We find tech-strength and innovation indexes wanting, but nevertheless, we include them in our analysis as data to proxy for technology and innovation is hard to come by.

As there is disparity in wealth, employment prospect, social equality, technology availability, innovation, education level and mindset in different countries or regions within a country, our defined knowledge economy can only be found in a few countries.

Acknowledgements

We would like to express our thanks of gratitude to the editor, Prof Elias G. Carayannis, Editor-in-Chief, for his comments as well as an anonymous reviewer who have for their insightful suggestions and careful reading of the manuscript.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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The US economy is flashing a recession warning that has only been wrong once in the last century, top economist says

The economy is flashing a recession warning that has only been wrong once in the last 120 years.
The ECRI's Leading Economic Index has started to decline in the past year, top economist Lakshman Achuthan said.
GDP growth and the job market are also weakening in certain areas, which could lead to trouble for the US, he added.

The US economy is flashing a classic recession warning that has only shown a false positive once in the last century, according to top economist Lakshman Achuthan.

The business-cycle expert and cofounder of the Economic Cycle Research Institute pointed to troubling signs of weakness in the US, with warning signs of a downturn cropping up in multiple areas of the economy.

The ECRI's Leading Economic Index — the economic indicator with a near-perfect track record — has started to decline over the last year, Achuthan said, speaking in a webcast with Rosenberg Research on Wednesday.

Watch: How tech layoffs could affect the economy

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Evolution of the Knowledge Economy: a Historical Perspective with an Application to the Case of Europe

Published: 05 July 2015
Volume 8 , pages 159–176, ( 2017 )

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Jadranka Švarc 1 &
Marina Dabić 2 , 3

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“The empires of the future are the empires of the mind.” Sir Winston Churchill Speech at Harvard, 6 September 1943, in Onwards to Victory (1944)

The goal of the article is to explore the evolution of original concept of knowledge economy based on science intensive production sectors toward service type economies which significantly changed the role of scientific research and technological innovation for economic growth. The paper argues that this transition is due not only to the structural changes in global production, but the theoretical evolution and paradigmatic shift of the concept of “knowledge economy” in general and “knowledge” in particular has played a significant role. The paper examines the different interpretation of knowledge within new types of intangible economies (e.g., new/Internet, weightless, service, creative, cultural economies) where knowledge is perceived to be generated not as a product of scientific research but as a service or creative activity and critically examined the role of scientific research in a service led knowledge economy. Additionally, the paper argue how these phenomena, which marked the global economy in the last decades, enable the transition of the standard concept of knowledge economy originated from industrial production and manufacturing to a knowledge economy equalized with various types of expanding intangible economies, primarily those based on service and creative industries.

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Knowledge and Innovation Economy: an Evaluation of Turkey

Knowledge Intensive Services

A Knowledge-Based Economy

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Švarc, J., Dabić, M. Evolution of the Knowledge Economy: a Historical Perspective with an Application to the Case of Europe. J Knowl Econ 8 , 159–176 (2017). https://doi.org/10.1007/s13132-015-0267-2

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