the further research directions

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• GETTING STARTED
• Introduction
• FUNDAMENTALS
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• Concepts, constructs & variables
• Research limitations
• Getting started
• Sampling Strategy
• Research Quality
• Research Ethics
• Data Analysis

FUTURE RESEARCH

Types of future research suggestion.

The Future Research section of your dissertation is often combined with the Research Limitations section of your final, Conclusions chapter. This is because your future research suggestions generally arise out of the research limitations you have identified in your own dissertation. In this article, we discuss six types of future research suggestion. These include: (1) building on a particular finding in your research; (2) addressing a flaw in your research; examining (or testing) a theory (framework or model) either (3) for the first time or (4) in a new context, location and/or culture; (5) re-evaluating and (6) expanding a theory (framework or model). The goal of the article is to help you think about the potential types of future research suggestion that you may want to include in your dissertation.

Before we discuss each of these types of future research suggestion, we should explain why we use the word examining and then put or testing in brackets. This is simply because the word examining may be considered more appropriate when students use a qualitative research design; whereas the word testing fits better with dissertations drawing on a quantitative research design. We also put the words framework or model in brackets after the word theory . We do this because a theory , framework and model are not the same things. In the sections that follow, we discuss six types of future research suggestion.

Addressing research limitations in your dissertation

Building on a particular finding or aspect of your research, examining a conceptual framework (or testing a theoretical model) for the first time, examining a conceptual framework (or testing a theoretical model) in a new context, location and/or culture.

• Expanding a conceptual framework (or testing a theoretical model)

Re-evaluating a conceptual framework (or theoretical model)

In the Research Limitations section of your Conclusions chapter, you will have inevitably detailed the potential flaws (i.e., research limitations) of your dissertation. These may include:

An inability to answer your research questions

Theoretical and conceptual problems

Limitations of your research strategy

Problems of research quality

Identifying what these research limitations were and proposing future research suggestions that address them is arguably the easiest and quickest ways to complete the Future Research section of your Conclusions chapter.

Often, the findings from your dissertation research will highlight a number of new avenues that could be explored in future studies. These can be grouped into two categories:

Your dissertation will inevitably lead to findings that you did not anticipate from the start. These are useful when making future research suggestions because they can lead to entirely new avenues to explore in future studies. If this was the case, it is worth (a) briefly describing what these unanticipated findings were and (b) suggesting a research strategy that could be used to explore such findings in future.

Sometimes, dissertations manage to address all aspects of the research questions that were set. However, this is seldom the case. Typically, there will be aspects of your research questions that could not be answered. This is not necessarily a flaw in your research strategy, but may simply reflect that fact that the findings did not provide all the answers you hoped for. If this was the case, it is worth (a) briefly describing what aspects of your research questions were not answered and (b) suggesting a research strategy that could be used to explore such aspects in future.

You may want to recommend that future research examines the conceptual framework (or tests the theoretical model) that you developed. This is based on the assumption that the primary goal of your dissertation was to set out a conceptual framework (or build a theoretical model). It is also based on the assumption that whilst such a conceptual framework (or theoretical model) was presented, your dissertation did not attempt to examine (or test) it in the field . The focus of your dissertations was most likely a review of the literature rather than something that involved you conducting primary research.

Whilst it is quite rare for dissertations at the undergraduate and master's level to be primarily theoretical in nature like this, it is not unknown. If this was the case, you should think about how the conceptual framework (or theoretical model) that you have presented could be best examined (or tested) in the field . In understanding the how , you should think about two factors in particular:

What is the context, location and/or culture that would best lend itself to my conceptual framework (or theoretical model) if it were to be examined (or tested) in the field?

What research strategy is most appropriate to examine my conceptual framework (or test my theoretical model)?

If the future research suggestion that you want to make is based on examining your conceptual framework (or testing your theoretical model) in the field , you need to suggest the best scenario for doing so.

More often than not, you will not only have set out a conceptual framework (or theoretical model), as described in the previous section, but you will also have examined (or tested) it in the field . When you do this, focus is typically placed on a specific context, location and/or culture.

If this is the case, the obvious future research suggestion that you could propose would be to examine your conceptual framework (or test the theoretical model) in a new context, location and/or culture. For example, perhaps you focused on consumers (rather than businesses), or Canada (rather than the United Kingdom), or a more individualistic culture like the United States (rather than a more collectivist culture like China).

When you propose a new context, location and/or culture as your future research suggestion, make sure you justify the choice that you make. For example, there may be little value in future studies looking at different cultures if culture is not an important component underlying your conceptual framework (or theoretical model). If you are not sure whether a new context, location or culture is more appropriate, or what new context, location or culture you should select, a review the literature will often help clarify where you focus should be.

Expanding a conceptual framework (or theoretical model)

Assuming that you have set out a conceptual framework (or theoretical model) and examined (or tested) it in the field , another series of future research suggestions comes out of expanding that conceptual framework (or theoretical model).

We talk about a series of future research suggestions because there are so many ways that you can expand on your conceptual framework (or theoretical model). For example, you can do this by:

Examining constructs (or variables) that were included in your conceptual framework (or theoretical model) but were not focused.

Looking at a particular relationship aspect of your conceptual framework (or theoretical model) further.

Adding new constructs (or variables) to the conceptual framework (or theoretical model) you set out (if justified by the literature).

It would be possible to include one or a number of these as future research suggestions. Again, make sure that any suggestions you make have are justified , either by your findings or the literature.

With the dissertation process at the undergraduate and master's level lasting between 3 and 9 months, a lot a can happen in between. For example, a specific event (e.g., 9/11, the economic crisis) or some new theory or evidence that undermines (or questions) the literature (theory) and assumptions underpinning your conceptual framework (or theoretical model). Clearly, there is little you can do about this. However, if this happens, reflecting on it and re-evaluating your conceptual framework (or theoretical model), as well as your findings, is an obvious source of future research suggestions.

How to Write the Discussion Section of a Research Paper

The discussion section of a research paper analyzes and interprets the findings, provides context, compares them with previous studies, identifies limitations, and suggests future research directions.

Updated on September 15, 2023

Structure your discussion section right, and you’ll be cited more often while doing a greater service to the scientific community. So, what actually goes into the discussion section? And how do you write it?

The discussion section of your research paper is where you let the reader know how your study is positioned in the literature, what to take away from your paper, and how your work helps them. It can also include your conclusions and suggestions for future studies.

First, we’ll define all the parts of your discussion paper, and then look into how to write a strong, effective discussion section for your paper or manuscript.

Discussion section: what is it, what it does

The discussion section comes later in your paper, following the introduction, methods, and results. The discussion sets up your study’s conclusions. Its main goals are to present, interpret, and provide a context for your results.

What is it?

The discussion section provides an analysis and interpretation of the findings, compares them with previous studies, identifies limitations, and suggests future directions for research.

This section combines information from the preceding parts of your paper into a coherent story. By this point, the reader already knows why you did your study (introduction), how you did it (methods), and what happened (results). In the discussion, you’ll help the reader connect the ideas from these sections.

Why is it necessary?

The discussion provides context and interpretations for the results. It also answers the questions posed in the introduction. While the results section describes your findings, the discussion explains what they say. This is also where you can describe the impact or implications of your research.

Adds context for your results

Most research studies aim to answer a question, replicate a finding, or address limitations in the literature. These goals are first described in the introduction. However, in the discussion section, the author can refer back to them to explain how the study's objective was achieved. 

Shows what your results actually mean and real-world implications

The discussion can also describe the effect of your findings on research or practice. How are your results significant for readers, other researchers, or policymakers?

What to include in your discussion (in the correct order)

A complete and effective discussion section should at least touch on the points described below.

Summary of key findings

The discussion should begin with a brief factual summary of the results. Concisely overview the main results you obtained.

Begin with key findings with supporting evidence

Your results section described a list of findings, but what message do they send when you look at them all together?

Your findings were detailed in the results section, so there’s no need to repeat them here, but do provide at least a few highlights. This will help refresh the reader’s memory and help them focus on the big picture.

Read the first paragraph of the discussion section in this article (PDF) for an example of how to start this part of your paper. Notice how the authors break down their results and follow each description sentence with an explanation of why each finding is relevant. 

State clearly and concisely

Following a clear and direct writing style is especially important in the discussion section. After all, this is where you will make some of the most impactful points in your paper. While the results section often contains technical vocabulary, such as statistical terms, the discussion section lets you describe your findings more clearly. 

Interpretation of results

Once you’ve given your reader an overview of your results, you need to interpret those results. In other words, what do your results mean? Discuss the findings’ implications and significance in relation to your research question or hypothesis.

Analyze and interpret your findings

Look into your findings and explore what’s behind them or what may have caused them. If your introduction cited theories or studies that could explain your findings, use these sources as a basis to discuss your results.

For example, look at the second paragraph in the discussion section of this article on waggling honey bees. Here, the authors explore their results based on information from the literature.

Unexpected or contradictory results

Sometimes, your findings are not what you expect. Here’s where you describe this and try to find a reason for it. Could it be because of the method you used? Does it have something to do with the variables analyzed? Comparing your methods with those of other similar studies can help with this task.

Context and comparison with previous work

Refer to related studies to place your research in a larger context and the literature. Compare and contrast your findings with existing literature, highlighting similarities, differences, and/or contradictions.

How your work compares or contrasts with previous work

Studies with similar findings to yours can be cited to show the strength of your findings. Information from these studies can also be used to help explain your results. Differences between your findings and others in the literature can also be discussed here. 

How to divide this section into subsections

If you have more than one objective in your study or many key findings, you can dedicate a separate section to each of these. Here’s an example of this approach. You can see that the discussion section is divided into topics and even has a separate heading for each of them. 

Limitations

Many journals require you to include the limitations of your study in the discussion. Even if they don’t, there are good reasons to mention these in your paper.

Why limitations don’t have a negative connotation

A study’s limitations are points to be improved upon in future research. While some of these may be flaws in your method, many may be due to factors you couldn’t predict.

Examples include time constraints or small sample sizes. Pointing this out will help future researchers avoid or address these issues. This part of the discussion can also include any attempts you have made to reduce the impact of these limitations, as in this study .

How limitations add to a researcher's credibility

Pointing out the limitations of your study demonstrates transparency. It also shows that you know your methods well and can conduct a critical assessment of them.  

Implications and significance

The final paragraph of the discussion section should contain the take-home messages for your study. It can also cite the “strong points” of your study, to contrast with the limitations section.

Restate your hypothesis

Remind the reader what your hypothesis was before you conducted the study. 

How was it proven or disproven?

Identify your main findings and describe how they relate to your hypothesis.

How your results contribute to the literature

Were you able to answer your research question? Or address a gap in the literature?

Future implications of your research

Describe the impact that your results may have on the topic of study. Your results may show, for instance, that there are still limitations in the literature for future studies to address. There may be a need for studies that extend your findings in a specific way. You also may need additional research to corroborate your findings. 

Sample discussion section

This fictitious example covers all the aspects discussed above. Your actual discussion section will probably be much longer, but you can read this to get an idea of everything your discussion should cover.

Our results showed that the presence of cats in a household is associated with higher levels of perceived happiness by its human occupants. These findings support our hypothesis and demonstrate the association between pet ownership and well-being. 

The present findings align with those of Bao and Schreer (2016) and Hardie et al. (2023), who observed greater life satisfaction in pet owners relative to non-owners. Although the present study did not directly evaluate life satisfaction, this factor may explain the association between happiness and cat ownership observed in our sample.

Our findings must be interpreted in light of some limitations, such as the focus on cat ownership only rather than pets as a whole. This may limit the generalizability of our results.

Nevertheless, this study had several strengths. These include its strict exclusion criteria and use of a standardized assessment instrument to investigate the relationships between pets and owners. These attributes bolster the accuracy of our results and reduce the influence of confounding factors, increasing the strength of our conclusions. Future studies may examine the factors that mediate the association between pet ownership and happiness to better comprehend this phenomenon.

This brief discussion begins with a quick summary of the results and hypothesis. The next paragraph cites previous research and compares its findings to those of this study. Information from previous studies is also used to help interpret the findings. After discussing the results of the study, some limitations are pointed out. The paper also explains why these limitations may influence the interpretation of results. Then, final conclusions are drawn based on the study, and directions for future research are suggested.

How to make your discussion flow naturally

If you find writing in scientific English challenging, the discussion and conclusions are often the hardest parts of the paper to write. That’s because you’re not just listing up studies, methods, and outcomes. You’re actually expressing your thoughts and interpretations in words.

• How formal should it be?
• What words should you use, or not use?
• How do you meet strict word limits, or make it longer and more informative?

Always give it your best, but sometimes a helping hand can, well, help. Getting a professional edit can help clarify your work’s importance while improving the English used to explain it. When readers know the value of your work, they’ll cite it. We’ll assign your study to an expert editor knowledgeable in your area of research. Their work will clarify your discussion, helping it to tell your story. Find out more about AJE Editing.

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SYSTEMATIC REVIEW article

Knowledge hiding: current research status and future research directions.

• 1 Business School, Huaqiao University, Quanzhou, China
• 2 Department of Management, Kedge Business School, Talence, France
• 3 Business School, Beijing Normal University, Beijing, China

This article provides a review of scientific articles addressing the topic of knowledge hiding in organizations. Based on a descriptive analysis, bibliometric analysis, and content analysis of a sample of 81 articles published in the academic journals in the Web of Science from 2012 to 2020, we identify the main areas and current dynamics of knowledge hiding research. Our results show that the central research themes of knowledge hiding include five clusters: concept and dimensions, antecedents, consequences, theories, and influence mechanisms. Based on our findings, we suggest future research should further develop the concept and dimensions of knowledge hiding; probe deeper into the consequences of knowledge hiding; explore multilateral, cross-level, and collective knowledge hiding; employ innovative theoretical perspectives and research methods to study knowledge hiding; and address how cultural and other contextual factors may shape the knowledge hiding behavior.

Introduction

Knowledge management plays a crucial role in each organization, which can affect the firms' and employees' performance. However, due to the practice of “knowledge hiding,” it is often challenging to achieve satisfactory results in knowledge management ( Connelly and Kelloway, 2003 ). Previous research has pointed out that employees are not willing to share knowledge, due to reasons such as protection and control of knowledge ownership, expertise dominance, and defensive awareness ( Huo et al., 2016 ). About 50% of employees have the intention to withhold, mislead, or conceal knowledge that has been requested by another person ( Peng, 2013 ). This behavior of deliberately not providing the required knowledge to colleagues when requested is called “knowledge hiding” ( Connelly et al., 2012 ), which has become an independent concept that is different from the opposite side of knowledge sharing ( Zhao et al., 2019 ).

Obviously, knowledge hiding is very likely to reduce the efficiency of knowledge exchange among members, hinder the generation of new ideas/thoughts, or even destroy trust ( Connelly et al., 2012 ), increasing the risk of knowledge loss and inhibiting the creativity of individuals and teams ( Cerne et al., 2014 ; Bogilović et al., 2017 ). Along this vein, it makes sense to solve the dilemma of insufficient knowledge sharing through the elimination of knowledge hiding, facilitating knowledge conversion within organizations. As a result, based on a descriptive analysis, bibliometric analysis, and content analysis, we conduct an in-depth analysis of knowledge hiding publications in international Science Citation Index (SCI) and Social Science Citation Index (SSCI) journals. We aim to address these research questions:

1. What is the current publication trend in knowledge hiding?

2. Which themes involving knowledge hiding have been studied by scholars?

3. What are the areas involving knowledge hiding that seem to require future research?

Previous authors have conducted reviews on knowledge hiding (e.g., Xiao and Cooke, 2019 ; Anand et al., 2020 ; de Garcia et al., 2020 ), which are valuable. However, the review of Xiao and Cooke (2019) is based on 52 articles and all of which are written in English or Chinese, and published over the period 1997–2017. Similarly, the review of Anand et al. (2020) is drawing on 52 studies. In their work, de Garcia et al. (2020) have reviewed a total of 57 articles that are published up to April 2018, and their study focuses on distinguishing knowledge hiding and knowledge hoarding from knowledge collection and donation perspectives. Our review differs from these previous works in terms of volume, timeframe, method and the analysis. First, we have combined bibliometric analysis, content analysis and descriptive analysis in this review, which allows for incorporating rich data with less interpretative or subjectivity biases. In contrast to previous reviews, we further overview the concepts and dimensions, antecedents, consequences, theoretical foundations, and influence mechanisms of knowledge hiding. In the meantime, we have included bigger volume of articles in this review. In so doing, we are able to complement the previous reviews, offering a more objective account of evolution of this research topic.

Methodology

Our study has followed the systematic review process ( Pickering and Byrne, 2014 ). Within this process, we employ the principles of Tranfield et al. (2003) , which include (1) setting the scope, (2) conducting the search and data extraction, (3) selecting the studies and analyzing the data, and (4) extracting data and reporting the findings. To ensure the data validity and reliability, we limited our databases by searching the sample of English-written articles from the Web of Science over the period between 1995 and 2020. Further, the main reason for using SCI and SSCI databases is that web of science is “generally considered credible among the scientific community, and [are] commonly used by researchers from a wide range of fields ( de Garcia et al., 2020 , p. 4). Several reviews have used these databases (e.g., Bernatović et al., 2021 ; Vlačić et al., 2021 ).

Retrieval conditions were “Title = knowledge hiding” or “Title = knowledge withholding,” and the time span was “All years (1950–2020).” The database was “Web of Science Core Collection” and the search basis was “Web of Science Category = Unrestricted Category.” In total, we obtained a sample of 233 articles. Subsequent analysis of these 233 articles' abstracts was conducted. In order to ensure data accuracy, we carefully selected studies that fit the definition given by Connelly et al. (2012) and excluded those that belonged to disciplines such as information management. This yielded 81 articles related to knowledge hiding. For these 81 articles, we undertook the reading of full texts, using Excel to record the key findings, theoretical lens, and methodologies. Building upon the content extraction, the authors classified the core clusters in five main themes according to their characteristics: concept and dimensions, antecedents, consequences, theoretical frameworks, and influence mechanism. Figure 1 shows the flow diagram of analysis.

Figure 1 . Flow diagram.

Analysis and Findings

Publication by year.

The analysis of the number of publications per year on knowledge hiding in international journals (see Figure 2 ) shows that scholars started to systematically study knowledge hiding as an organizational behavior in the 2010s. A growing number of studies have addressed knowledge hiding but it dates back only to 2012, when knowledge hiding was first proposed as an independent concept in the work of Connelly et al. (2012) . Knowledge hiding research has gone through two periods: the initial stage (from 2012 to 2018) and the fast development stage (from 2019 to 2020). During the initial stage, publications on knowledge hiding in mainstream international journals were rare, and there were only between one and five articles published per year. Since 2019, there has been a sharp increase in knowledge hiding publications; the number of publications has jumped to more than 30 articles per year (see Figure 2 ).

Figure 2 . Annual distribution of articles on knowledge hiding.

Journal Distribution of Knowledge Hiding Research

From 2012 to 2020, research on knowledge hiding has been published in 43 SCI/SSCI journals (see Table 1 ), with 40 articles (49.38%) published in Journal Citation Reports (JCR) Q1 journals, 19 articles (23.46%) published in JCR Q2 journals, 8 articles (9.88%) published in JCR Q3 journals, and 11 articles (13.58%) published in JCR Q4 journals; 15 articles (18.52%) published in the Chartered Association of Business Schools (ABS3) journals, 10 articles (12.35%) published in ABS4 journals, one article (1.23%) published in Financial Times (FT50) journals; and one article (1.23%) published each in UT Dallas top 100 business school research rankings (UTD24) and ABS4 * journals. The top 10 journals that published most of the knowledge hiding articles are Journal of Knowledge Management, Journal of Organizational Behavior, Management Decision, International Journal of Hospitality Management, European Journal of Work and Organizational Psychology, Knowledge Management Research and Practice, International Journal of Information Management, Asian Business and Management, Leadership and Organization Development Journal , and Journal of Managerial Psychology . The majority of knowledge hiding research has been published in JCR Q1/Q2 journals, and a considerable proportion has been published in ABS3/4 journals.

Table 1 . Top publishing journals on knowledge hiding.

Publishing Activity by Authors, Authors' Institutions, and Locations

Knowledge hiding has attracted considerable attention from researchers and practitioners. As shown in Table 2 , Matej Cerne published the most articles (eight) on knowledge hiding followed by Škerlavaj and Connelly, with seven and six articles respectively. The most active institutions in the research field of knowledge hiding were University of Ljubljana (eight publications), followed by BI Norwegian Business School, McMaster University and Tongji University, each with seven publications. Table 3 lists the locations of authors' institutions, with the top four being China, Pakistan, Canada and United Arab Emirates.

Table 2 . Top publishing authors and institutions on knowledge hiding.

Table 3 . Publishing activity by authors' institution location.

Publishing Activity by Data Sources

Our analysis shows that previous data on knowledge hiding have tended to be collected in one single location, such as China, Pakistan, United Arab Emirates, Saudi Arabia, United States, and so on (see Table 4 ). Eight publications used data that were collected from multi-countries and regions (e.g., North America, Germany and Austria, Europe, Slovenia, Croatia, Serbia, Bosnia and Herzegovina, Montenegro and Macedonia). The top three locations from which researchers have collected knowledge hiding data were China (29 publications), Pakistan (13 publications) and United Arab Emirates (5 publications).

Table 4 . Locations from which researchers have collected knowledge hiding data.

Highly Cited Publications

Citations can show the research focus of scholars and reveal their main theoretical lens. Highly cited articles are often regarded as important references in the field. Table 5 presents the top 15 highly cited publications on knowledge hiding.

Table 5 . Top 15 articles on knowledge hiding by the number of citations.

Further, through a co-citation analysis, co-authorship analysis, keyword and co-occurrence analysis, and content analysis, we find that most research on knowledge hiding focuses on the concept and dimensions of the topic. For instance, as one of the highly cited publications, it is important to acknowledge that Connelly et al. (2012) take the lead in defining the concept of knowledge hiding and propose evasive hiding, playing dumb, and rationalized hiding as three dimensions of knowledge hiding. Based on the work of Connelly et al. (2012) ; Zhao et al. (2016) further examine the interpersonal antecedents of the three dimensions of knowledge hiding. Hernaus et al. (2019) distinguish the three dimensions of knowledge hiding and address how individual competitiveness may lead to knowledge hiding. Connelly and Zweig (2015) point out that the three dimensions of knowledge hiding are not equally and always harmful, where under certain circumstances, some knowledge hiding can be beneficial. Among the highly cited publications, scholars also focus on the antecedents of knowledge hiding, paying particular attention to workplace stressors, psychological ownership, and territoriality of knowledge. For example, Zhao et al. (2016) ; Škerlavaj et al. (2018) , and Khalid et al. (2018) have examined the influence mechanisms of workplace stressors, such as workplace ostracism, abusive supervision, and interpersonal injustice, on knowledge hiding. Peng (2013) ; Huo et al. (2016) , and Singh (2019) emphasize the predictive effect of psychological ownership and territoriality of knowledge on knowledge hiding. Serenko and Bontis (2016) ; Hernaus et al. (2019) , and Malik et al. (2019) also investigate the antecedents of knowledge hiding with different focuses (e.g., intra-organizational knowledge hiding, the individual-level and job-related factors within academia, organizational politics). These studies represent the two most important research directions of knowledge hiding.

Following, among the highly cited publications, we find that individual and team creativity, interpersonal relationships, and retaliation show the key consequences of knowledge hiding. The main contributions in the field include the work of Cerne et al. (2014) , who point out that “when employees hide knowledge, they trigger a reciprocal distrust loop in which coworkers are unwilling to share knowledge with them” (p. 172). In recent years, Connelly and Zweig (2015) , and Serenko and Bontis (2016) also prove that knowledge hiding can lead to retaliation. Cerne et al. (2017) and Malik et al. (2019) examine the destructive effect of knowledge hiding on individual creativity. Bogilović et al. (2017 ) and Fong et al. (2018) analyze the impacts of individual-level knowledge hiding on team-level creativity. These studies represent the mainstream consequences of knowledge hiding.

Additionally, we identify that the research focus on knowledge hiding has moved from the individual level to a multilevel influence mechanism. For example, Huo et al. (2016) ; Cerne et al. (2017) ; Fong et al. (2018) , and Hernaus et al. (2019) explore the moderating effect of team-level task interdependence on the relationship between individual-level variables and knowledge hiding. In addition, team-level cultural factors (e.g., mastery climate, workplace ethics) and organizational justice are variables that scholars have examined when exploring the multilevel influence mechanism of knowledge hiding ( Huo et al., 2016 ; Cerne et al., 2017 ; Khalid et al., 2018 ).

Major Research Clusters and Topics

Using CiteSpace4.0 software, we conducted the descriptive analysis, bibliometric analysis, and content analysis of the 81 knowledge hiding articles that are published in the international journals from 2012 to 2020. In order to clearly demonstrate the current status of knowledge hiding research, we structure our findings into the following five clusters (see Figure 3 ).

Figure 3 . Research framework of knowledge hiding. Source: extended and developed from Connelly et al. (2012) and Xiao and Cooke (2019) .

Concept and Dimensions

The bibliometric analysis suggests that keywords related to the concept of knowledge hiding include knowledge sharing, knowledge withholding, and knowledge management process. Based on these keywords and the results of our content analysis, we extract “concept and dimensions” as the first cluster that reflects the research interests in knowledge hiding.

The concept of knowledge hiding was first defined as the act of deliberately not providing knowledge or providing knowledge that is not what the seeker needs when facing a colleague's request ( Connelly et al., 2012 ). These were the first authors to discuss the linkages and differences between knowledge hiding and related concepts, such as knowledge sharing/non-sharing ( Anand et al., 2020 ), knowledge withholding ( Webster et al., 2008 ), knowledge hoarding ( Xiao and Cooke, 2019 ; de Garcia et al., 2020 ), counterproductive/deviant behavior ( Connelly and Zweig, 2015 ; Serenko and Bontis, 2016 ), workplace deception ( Connelly et al., 2012 ), and incivility ( Zhao et al., 2016 ). Later, scholars further proposed concepts such as knowledge sharing hostility ( Stenius et al., 2016 ), disengagement from knowledge sharing ( Zhao et al., 2016 ), knowledge contribution loafing ( Fang, 2017 ), and knowledge manipulation ( Bogilović et al., 2017 ). In recent years, scholars have tried to differentiate knowledge hiding from other related concepts (e.g., employee silence and knowledge protection) ( Bari et al., 2020 ).

In order to distinguish these different concepts, we compare relevant concepts through questioning whether knowledge seeking exists, the degree of knowledge sharing, and the intentionality of the behavior (see Figure 4 ). In general, scholars have widely accepted the definition of knowledge hiding given by Connelly et al. (2012) . The mainstream view believes that knowledge hiding is an important aspect of knowledge withholding, and it is not the opposite of knowledge sharing ( Connelly et al., 2012 ; Serenko and Bontis, 2016 ; Zhao et al., 2016 ). Consequently, one cannot simply equate knowledge hiding with non-sharing or a lack of knowledge sharing. In addition to subjective intention, the reasons that individuals do not share knowledge with others can be related to a lack of relevant knowledge or the inability to share the knowledge. It is worth pointing out that there are different opinions in boundaries between knowledge hiding and concepts such as knowledge non-sharing, counterproductive knowledge behavior, and knowledge protection. Hence, there still exists some confusion and cross-use of related concepts in the knowledge hiding research. In addition, the existing literature has seldom defined knowledge hiding from the indigenous/cross-cultural perspective.

Figure 4 . Comparison between knowledge hiding and related concepts. Source: extended and developed from Connelly et al. (2012) and de Garcia et al. (2020) .

Connelly et al. (2012) have developed three dimensions of knowledge hiding and an employee self-evaluation scale with 12 items, with each dimension measuring four items. Among them, evasive hiding means that the hider provides invalid knowledge or pretends to agree to help, but lacks follow-up action. An example item is “I agreed to help him/her but never really intended to.” Playing dumb refers to pretending to be ignorant of the relevant knowledge or not understanding the knowledge seeker's question, with a sample item “I pretended I did not know what he/she was talking about.” Rationalized hiding means that the hider explains the reasons for not providing required knowledge, such as the necessity to keep it confidential or offering that knowledge sharing is not allowed by the superiors. An example item is “I explained that the information is confidential and available only to people on a particular project.” Most scholars believe that rationalized hiding is different in nature from evasive hiding and playing dumb, because rationalized hiding does not involve deception, but the evasive hiding and playing dumb do have a high degree of deception.

The scale of Connelly et al. (2012) has been proved to have high reliability and validity in a series of empirical studies. In general, scholars use this scale and its original items directly, making some contextual adaptation of expressions only according to the particular research needs. There are other knowledge hiding scales, such as Peng's ( Peng, 2013 ) three-item counterproductive knowledge behavior scale and knowledge withholding behavior scales developed by Lin and Huang (2010) ; Tsay et al. (2014) , and Serenko and Bontis (2016) . Anand et al. (2020) have advocated that knowledge hiding is composed of unintentional hiding (driven by contingent situation), motivational hiding (driven by performance and competition), controlled hiding (driven by psychological ownership), victimized hiding (driven by hostility and abuse), and favored hiding (driven by identity and norms). Jha and Varkkey (2018) identify the four strategies adopted by supervisors to hide knowledge from subordinates, namely, playing innocent, misleading, rationalized hiding, and counter-questioning.

Antecedents

The antecedents of knowledge hiding include the Big Five personality traits, abusive supervision, negative workplace gossip, and career insecurity. Combined with the research framework of knowledge hiding (see Figure 3 ), the second cluster as antecedents is popular among scholars. Inspired by the work of Connelly et al. (2012) and Xiao and Cooke (2019) , we review knowledge hiding antecedents from four aspects: knowledge characteristics, individual factors, team and interpersonal factors, and organizational factors.

Knowledge characteristic is one of the first antecedents popular among scholars. Due to the complex nature of knowledge, Connelly et al. (2012) point out that such complexity affects the willingness of individuals to provide help when facing colleagues' knowledge requests. Simply, it often requires more time and energy to generate complex knowledge that knowledge owners tend to keep the knowledge for themselves. Hernaus et al. (2019) argue that people are more likely to hide tacit knowledge rather than explicit knowledge. In addition, the task relevance and the value of knowledge have a positive relation with knowledge hiding ( Connelly et al., 2012 ; Huo et al., 2016 ).

Individual factors mainly include personality traits and psychological factors such as emotion and cognition. In terms of personality traits, scholars focus mainly on the influence of the Big Five personality traits, in particular neuroticism. For example, Pan and Zhang (2018) reveal that employees with high conscientiousness and low neuroticism are less likely to hide knowledge, while people with high neuroticism are more likely to hide knowledge ( Anaza and Nowlin, 2017 ). Pan et al. (2018) verify the effects of a “dark triad of personality” (Machiavellianism, narcissism, and psychopathy) on different dimensions of knowledge hiding. Fang (2017) and Aljawarneh and Atan (2018) examine the relationship between anxiety and knowledge hiding and the relationship between cynicism and knowledge hiding.

When it comes to the cognitive perception, prior research has focused mainly on the individual's self-efficacy, territoriality and psychological ownership, psychological safety, psychological contract breach, perceived pressure or job insecurity, perceived workplace status, and career prospects. Tsay et al. (2014) ; Jha and Varkkey (2018) , and Hernaus et al. (2019) argue that individuals' confidence in their knowledge and perception of their competitiveness affect their willingness to share knowledge. Peng (2013) ; Huo et al. (2016) ; Kang (2016) ; Singh (2019) ; Khalid et al. (2020) , and Zhai et al. (2020) believe individuals' perceived exclusivity of knowledge, knowledge power, and knowledge privacy are the primary factors that determine how much knowledge they are willing to share with colleagues. He et al. (2020) ; Lin et al. (2020) , and Wu (2020) explore the formation mechanism of knowledge hiding from the perspectives of psychological safety and perceived threats. Pradhan et al. (2019) ; Ghani et al. (2020a) , and Jahanzeb et al. (2020a) emphasize the negative impacts of employee psychological contract breaches on knowledge sharing in the organizations. Jha and Varkkey (2018) ; Škerlavaj et al. (2018) , and Feng and Wang (2019) examine the impacts of workplace stressors, such as time pressure and job insecurity, on knowledge hiding.

Prior studies have also investigated knowledge hiding from employee and supervisor perspectives. In their work, Butt (2019) and Butt and Ahmad (2019) show that concerns about career prospects are important individual-level reasons for supervisors to hide knowledge from subordinates. Liu et al. (2020) find that perceived workplace status affects knowledge hiding through two opposing mechanisms: perception of knowledge sharing obligation and perception of being envied. The goal orientation has also attracted some scholars' attention in recent years when studying knowledge hiding behavior. Research by Zhu et al. (2019) shows that performance-driven goal orientation has a positive relationship with employees' knowledge hiding behaviors, which allows employees to achieve the competitive goal of surpassing colleagues. Nadeem et al. (2021) argue that shared goals are negatively related to knowledge hiding. Moh'd et al. (2021) analyze the relationship between achievement goal orientation (e.g., learning goals, performance display/performance-avoidance goal orientation) and knowledge hiding. Some scholars highlight that individual motivational factors (such as expected results/rewards and perceived knowledge sharing costs) affect knowledge hiding ( Lin and Huang, 2010 ; Shen et al., 2019 ). Although emotion and cognition have been regarded as the two core elements that drive individual behavior (e.g., Lee and Allen, 2002 ), studies on how emotional/affective factors influence knowledge hiding are still underdeveloped. We believe only Zhao and Xia (2019) have studied the negative emotional state of nursing staff as the antecedent of their knowledge hiding behavior.

Team-level and interpersonal factors reflect leadership, interpersonal relationships, and their respective interactions. When considering leadership, scholars pay the most attention to abusive leadership, followed by ethical leadership. Khalid et al. (2018) point out that knowledge hiding is not necessarily an employee's intention to directly harm other organization members, but a negative reaction of employees to abusive supervision. Further, as indicated by displaced aggression theory, when employees encounter abusive leaders, they are more likely to retaliate by targeting innocent victims, namely, their colleagues but not the leaders. Based on the reactance theory, Feng and Wang (2019) point out that when employees experience frustration resulting from the abuse of their supervisors, they will take revenge in a direct or indirect way so that they can maintain a sense of freedom. However, because of their supervisors' supreme power and status in organizations, employees usually do not directly retaliate against supervisors so as not to cause stronger hostility and reciprocal retaliation. Ethical leadership can also influence employees' behavior intentionally or unintentionally through the role model effect. Abdullah et al. (2019) ; Anser et al. (2020) , and Men et al. (2020) argue a significant but negative correlation between ethical leadership and subordinates' knowledge hiding behavior. Interestingly, the study by Xia et al. (2019) describes an inverted U–shaped curve relationship between knowledge leadership and employee knowledge hiding. Through a multilevel model, Lin et al. (2020) find that individual-focused empowering leadership can improve the supervisor-subordinate relationship and therefore inhibit knowledge hiding, whereas differentiated empowering leadership can cause group relational conflict and then lead to knowledge hiding. Based on social exchange theories, Abdillah et al. (2020) argue that altruistic leaders' humility, patience, understanding, sympathy, and compassion will be perceived by employees as uniquely socio-emotional resources, which can enhance the positive emotion of employees, improve the quality of the exchange between supervisors and subordinates (obtaining the trust and respect of the subordinates), and encourage employees be willing to make extra efforts for the organization and eliminate selfish behaviors that harm the interests of the organization, thus effectively preventing employee knowledge hiding behaviors.

From the perspective of interpersonal abuse, prior research shows that employees who encounter interpersonal unfair treatment are less willing to share their personal knowledge assets with others ( Abubakar et al., 2019 ), whereas fair interpersonal interaction is significantly negatively correlated with the three dimensions of knowledge hiding ( Ghani et al., 2020b ). Among these, the factor of passive-aggressiveness in the workplace attracts more attention from scholars. Aljawarneh and Atan (2018) find that incivility in the workplace can drive employees to feel cynical and thus hide knowledge as a countermeasure. Zhao et al. (2016) and Riaz et al. (2019) point out that, as a typical workplace passive-aggressiveness, workplace ostracism would significantly increase employees' deceptive knowledge hiding (e.g., evasive hiding and playing dumb). Similarly, research by Yao et al. (2020a , b ) shows that negative interpersonal experiences, such as workplace bullying and negative workplace gossip, accelerate the exhaustion of employee resources, such as emotions, time, energy, and organizational identity, leading them to hide knowledge. Anand et al. (2020) also find that hostility and abusive colleagues/supervisors drive employees to hide knowledge.

Concerning the impacts of interpersonal relationship on knowledge hiding, current research has focused on exploring the effects of supervisor-subordinate relationships. Scholars first divide supervisor-subordinate relationships into formal work-related relationships (contractual relationship, Leader-Member Exchange) and informal non-work-related relationships (Chinese personal guanxi relationships, Supervisor-Subordinate Guanxi) ( He et al., 2020 ), or into economic LMX and social LMX ( Babič et al., 2019 ), and then explore their impacts on employees' knowledge hiding behaviors. Previous research reveals that LMX negatively affects evasive hiding and playing dumb ( Zhao et al., 2019 ). However, this reciprocal social exchange is more likely to reduce the level of knowledge hiding within the team, especially when the relationship between individuals and their supervisors has social LMX characteristics ( Cerne et al., 2014 ). Furthermore, upward LMX social comparison leads to envy among team members, so it is a potential interpersonal antecedent of knowledge hiding among colleagues ( Weng et al., 2020 ). It is worth noting that team prosocial motivation and social LMX (but not economic LMX) have an interaction effect on knowledge hiding ( Babič et al., 2019 ). Lin and Huang (2010) ; Butt (2019) ; Butt and Ahmad (2019) ; Semerci (2019) examine the influences of interpersonal factors such as trust, reciprocity, relationship recognition, lack of interpersonal relationship, relationship conflict, and interpersonal competition. Interestingly, Lin and Huang (2010) point out that emotional bonds such as trust and reciprocity among team members can make individuals give up hiding too much knowledge to avoid retaliation from others. In addition, task conflicts and relationship conflicts have additive effects on knowledge hiding ( Semerci, 2019 ).

At the organizational level, scholars have explored the roles of organizational culture, knowledge management policies and systems, organizational politics, organizational justice, organizational recognition, and a competitive performance environment on employees' conduct of knowledge hiding. First, the knowledge sharing culture has been proved to be closely related to the extent to which the knowledge hiding behavior can be accepted and adopted by the members of the organization ( Connelly et al., 2012 ). For example, Anaza and Nowlin (2017) point out that the lack of incentives for knowledge sharing and the lack of supervisor feedback on subordinates' knowledge sharing will lead employees to hide knowledge. Jha and Varkkey (2018) highlight that a lack of organizational recognition of knowledge sharing and workload increase due to knowledge sharing increase employee knowledge hiding.

Social norms, organization policies, and management systems have also been found to have a profound impact on employees' tendency to hide knowledge. For instance, Butt and Ahmad (2019) argue that knowledge hiding is deeply embedded in many local companies and is regarded as a common code of conduct in the United Arab Emirates. Serenko and Bontis (2016) find that organizational knowledge management systems and policies have a significant direct impact on employee knowledge hiding, whereas injustice prompts employees to spontaneously engage in knowledge hiding behavior. Malik et al. (2019) propose that perceived organizational politics positively predict knowledge hiding. Abubakar et al. (2019) find that distributional, procedural, and interactional injustice increase the level of knowledge hiding among employees. Research by Jahanzeb et al. (2020b) confirms that employees who encounter organizational unfairness consider knowledge hiding as a means to rationalize the cognitive separation between oneself and the organization in order to maintain one's dignity. Finally, some scholars have examined the impact of a competitive working environment. For example, Anaza and Nowlin (2017) explain how internal competition can lead to knowledge hiding. Similar findings can be found in the work of Anand et al. (2020) , who argue that organizational internal performance and competitive factors drive employees to hide knowledge.

Consequences

Based on the highly cited publications and the keyword analysis, we find that consequences, performance, behavior , and employee/team creativity are some keywords that reflect the outcome of knowledge hiding. Therefore, we use the term consequences to summarize the third cluster concerning the knowledge hiding research.

Current research focuses mainly on the individual- and team-level consequences of knowledge hiding. A small number of studies examine the individual-level consequences of knowledge hiding between supervisors and subordinates. In terms of individual-level results, the existing research has examined the effects of knowledge hiding on individual job performance, psychological status and attitude, workplace behavior, and supervisor-subordinate/coworker relationships. For instance, most studies have found that knowledge hiding among colleagues and between supervisors and subordinates can reduce task performance, organizational citizenship behavior (OCB), and creativity ( Connelly et al., 2012 ; Cerne et al., 2014 ; Arain et al., 2019 , 2020a , b ; Jahanzeb et al., 2019 ; Malik et al., 2019 ; Singh, 2019 ; Zhu et al., 2019 ).

However, there are some mixing findings. For example, Wang et al. (2019) argue that perceived colleague knowledge hiding does not reduce the performance of salespersons. Instead, it encourages them to work harder to improve their sales performance. Burmeister et al. (2019) find that knowledge hiding (playing dumb, in contrast to evasive hiding and rationalized hiding) has opposite effects on OCB, and knowledge hiders experience different emotions. Khoreva and Wechtler (2020) point out that evasive hiding is negatively related to in-role performance, and playing dumb is positively related to it. In addition, both evasive hiding and rationalized hiding will hinder innovation performance. Regarding psychological status and attitudes, research suggests that knowledge hiding increases employees' moral disengagement ( Arain et al., 2020a ) and decreases their psychological safety, well-being, job satisfaction, and sense of thriving ( Jiang et al., 2019 ; Offergelt et al., 2019 ; Khoreva and Wechtler, 2020 ). Furthermore, knowledge hiding can trigger knowledge seekers' deviant behaviors, turnover intention, upward silence, and non-engagement in knowledge sharing ( Connelly and Zweig, 2015 ; Offergelt et al., 2019 ; Singh, 2019 ; Arain et al., 2020a ).

Concerning interpersonal relationships, studies reveal that knowledge hiding among colleagues or between supervisors and subordinates can damage workplace relationships, which can even lead to a trust crisis ( Connelly et al., 2012 ; Cerne et al., 2014 ; Arain et al., 2020b ). In particular, Connelly et al. (2012) , Cerne et al. (2014) , and Connelly and Zweig (2015) highlight that knowledge hiding can result in a vicious circle of rejecting knowledge sharing. Studies also find that knowledge hiding has significant negative effects on team performance ( Zhang and Min, 2019 ), team creativity ( Fong et al., 2018 ; Bari et al., 2019 ), team viability ( Wang et al., 2019 ), team learning, and absorptive capability ( Fong et al., 2018 ; Zhang and Min, 2019 ).

In summary, scholars have made advancements on the impacts of knowledge hiding on the individual level, but research on its impacts on team and organizational levels is still at a nascent stage. Few scholars have recently analyzed the “boomerang effect” or “negative reinforcement cycle” of knowledge hiding—the impact of knowledge hiding on the hiders' psychological status, job performance, and creativity (e.g., Cerne et al., 2014 ; Jiang et al., 2019 )—and its double-edged sword effect ( Wang et al., 2019 ), which has opened up a new avenue for research.

Theoretical Perspectives

The fourth cluster concentrates on theories that are popular among scholars that they use to conduct knowledge hiding research. The theories applied in the field of knowledge hiding are mainly from two domains—managerial theory and psychological theory—and include theories such as “exchange” (represented by social exchange theory), “resources” [represented by Conservation of Resources (COR) Theory], “learning” (represented by social learning theory), “cognition” (represented by social cognitive theory), “ownership” (represented by psychological ownership theory), “goal orientation” (represented by achievement goal theory), “personality traits,” “job characteristics,” social identity theory, displaced aggression theory, and justice theory (see Table 6 ). Although scholars have introduced other theories to study knowledge hiding, the effectiveness of this theoretical development needs to be enhanced. For example, how to theorize individual emotions has not yet been made systematic and thus needs to be further explored in future research. Furthermore, we find that theories that are mostly used to examine the motivation/antecedents of knowledge hiding or the direct/indirect (mediating) influence of antecedent variables on knowledge hiding are less used to illustrate the consequences of knowledge hiding and the boundary conditions.

Table 6 . Theoretical perspectives used in knowledge hiding research.

Influence Mechanisms

There are findings on the mediating roles of antecedent variables that affect knowledge hiding. Emotional and cognitive factors (e.g., leadership, workplace stressors, interpersonal relationships, personality traits, and psychological ownership) can induce knowledge hiding. In terms of leadership, Abdullah et al. (2019) point out that ethical leadership inhibits employees' knowledge hiding by enhancing their relational social capital. Anser et al. (2020) find that the ethical behavior of ethical leaders can enhance the perception of “meaningful work” for service industries employees, thereby reducing the possibility of engaging in knowledge hiding behaviors. Khalid et al. (2018) find that perception of interpersonal justice mediates the relationship between abusive supervision and knowledge hiding. Feng and Wang (2019) believe that abusive supervision indirectly affects knowledge hiding through job insecurity. Pradhan et al. (2019) show that psychological contract breaching and the attacks toward supervisors play a partial mediating role in the process in which abusive supervision affects knowledge hiding. Ghani et al. (2020a) further point out that abusive supervision can easily lead to psychological contract breach, thus leading employees to attack their colleagues and deliberately hide knowledge from them. In addition, Lin et al. (2020) find that individual-focused empowering leadership enhances the psychological safety of subordinates, thereby reducing their knowledge hiding, whereas differentiated empowering leadership causes group relational conflicts, thereby increasing subordinate knowledge hiding. Abdillah et al. (2020) study the dual mediating mechanisms of altruistic leadership, which inhibits and prevents employees from knowledge hiding, pointing out that the positive emotions induced by altruistic leadership and LMX have important effects.

Regarding workplace stressors and interpersonal relationships, Aljawarneh and Atan (2018) find that cynicism mediates the relationship between tolerance of workplace incivility and knowledge hiding. Riaz et al. (2019) find that workplace ostracism has a significant impact on evasive hiding and playing dumb, and that work strain plays a mediating role. Yao et al. (2020a , b ) have shown that relational identification and interpersonal trust play a chain-mediating role in the relationship between negative workplace gossip and knowledge hiding. At the same time, emotional exhaustion and organizational identification play a chain-mediating role in the relationship between workplace bullying and knowledge hiding. Jahanzeb et al. (2020b) believe that the experience of injustice causes employees to be psychologically separated from the organization and thus employees will show more knowledge hiding behaviors. Zhao et al. (2019) demonstrate that organizational identification mediates the negative impact of LMX on evasive hiding and playing dumb. Weng et al. (2020) point out that employees' upward LMX social comparison with their colleagues leads to envy of and knowledge hiding toward their colleagues. He et al. (2020) discover that psychological safety fully mediates the influence of LMX on knowledge hiding and partially mediates the influence of supervisor-subordinate guanxi on knowledge hiding.

Another aspect is shown through personality traits. Wang et al. (2014) find that perceived social identity mediates the relationship between the Big Five personality traits and knowledge hiding. Pan et al. (2018) examine the positive relationship between the “dark triad of personality” (Machiavellianism, narcissism, and psychopathy) and knowledge hiding, as well as the mediating effect of transactional psychological contracts on this relationship. Zhao and Xia (2019) point out that the negative affect states of nurses staff can “activate” their moral disengagement mechanism, allowing them to redefine their knowledge hiding behaviors as reasonable and acceptable, and thus exacerbating their knowledge hiding tendency. The final aspect is psychological ownership. Research by Peng (2013) and Huo et al. (2016) show that employees' psychological ownership of knowledge enhances their territorial awareness, which in turn causes them to hide knowledge from colleagues. Liu et al. (2020) confirm that the influence of workplace status on employee knowledge hiding is carried out through two opposite mechanisms: perceived knowledge sharing responsibility and envy. The former negatively mediates the relationship between the two, and the latter positively mediates it.

Some scholars have also studied the mediating effect of knowledge hiding. For instance, scholars examine the process through which knowledge hiding impairs individual or team creativity and innovation performance. Cerne et al. (2014) find that the knowledge hiding makes hiders reduce their own creativity, and colleague distrust plays a mediating role. Arain et al. (2019) show that supervisor knowledge hiding can reduce subordinates' self-efficacy and thus reduce their innovation. Khoreva and Wechtler (2020) point out that playing dumb and rationalized hiding can indirectly influence employee innovation performance through the mediating effect of well-being. Fong et al. (2018) confirm that a decrease in absorptive capacity is the key mediator in the relationship between knowledge hiding and team creativity. Zhang and Min (2019) state that team learning partially mediates the relationship between knowledge hiding and project team performance.

Moreover, researchers have studied the process through which knowledge hiding affects employees' subsequent interpersonal behaviors. For instance, Burmeister et al. (2019) find that guilt and shame play opposite mediating roles in the relationship between individual knowledge hiding and its subsequent interpersonal-oriented OCB. Arain et al. (2020b) point out that supervisor knowledge hiding negatively influences subordinates' OCB toward their supervisors, and subordinate distrust in their supervisors plays a mediating role. Supervisor knowledge hiding can also activate employee moral disengagement, prompting them to reduce OCB toward their supervisors and increase silence behaviors ( Arain et al., 2020a ). Jiang et al. (2019) suggest that knowledge hiding makes the hiders feel the insecurity of self-expression and interpersonal risk, thereby reducing their psychological safety and endangering their ability to thrive at work. Despite these advancements, it is necessary to develop a robust framework that integrates multipath models based on different innovative theoretical perspectives.

Regarding the moderating role of contextual factors on knowledge hiding, the existing research mainly explores the contingency influence of individual differences, job characteristics, team characteristics, and team/organizational climate. In terms of individual differences, some scholars find that organizational psychological ownership can effectively reduce the knowledge hiding resulting from territoriality ( Peng, 2013 ). Furthermore, psychological ownership significantly moderates the inverted U-shaped relationship between knowledge leadership and knowledge hiding. This curved relationship is more obvious among employees with high psychological ownership ( Xia et al., 2019 ). High psychological ownership can also minimize the impact of abusive supervision on knowledge hiding ( Ghani et al., 2020a ). Other scholars explore the boundary effect of positive traits, such as individualism/collectivist values ( Semerci, 2019 ), positive affectivity ( Jahanzeb et al., 2020a ), benevolence or tolerance ( Jahanzeb et al., 2020b ), prosocial motivations ( Škerlavaj et al., 2018 ), harmonious work enthusiasm ( Anser et al., 2020 ), professional commitment ( Malik et al., 2019 ), trust-related affect/cognition ( Nadeem et al., 2021 ), social skills ( Wang et al., 2019 ), and cultural intelligence ( Bogilović et al., 2017 ). In addition to these studies, scholars examine the impacts of negative traits on knowledge hiding, such as negative reciprocity ( Zhao et al., 2016 ; Jahanzeb et al., 2019 ), instrumental thinking ( Abdullah et al., 2019 ), hostile attribution bias ( Khalid et al., 2020 ), moral disengagement ( Zhao et al., 2016 ), and cynicism ( Jiang et al., 2019 ).

In relation to job characteristics, task interdependence has attracted a lot of attention. Huo et al. (2016) point out that task interdependence can reduce the territorial awareness and knowledge hiding caused by psychological ownership. Hernaus et al. (2019) find that task interdependence can help reduce the probability of employees' evasive knowledge hiding due to maintaining their competitiveness. Fong et al. (2018) show that task interdependence moderates the relationship between knowledge hiding and team absorptive capacity. Weng et al. (2020) suggest that the interdependence of cooperative and competitive goals has opposite moderating effects on the relationship between upward LMX social comparison and knowledge hiding. In addition, Pan and Zhang (2018) also analyze the influence of work autonomy on the intensity of the relationship between neuroticism and knowledge hiding.

Regarding the team/organizational climate, research shows that in an environment that values information exchange and cooperation, the negative influence of knowledge hiding will be greatly weakened. Accordingly, Cerne et al. (2014) study the boundary effect of the team achievement-motivation climate (e.g., performance climate and mastery climate) on the relationship between knowledge hiding and the decrease in the hider's creativity. They discover that the negative effect of knowledge hiding on the hider's creativity is reduced in a mastery climate. Furthermore, Cerne et al. (2017) find the moderating effects of mastery climate, task interdependence, and autonomy on the relationship between knowledge hiding and innovative work behavior. Bari et al. (2019) obtain similar findings which point out that a perceived mastery climate reduces the negative impact of evasive hiding and playing dumb on team creativity. Feng and Wang (2019) find that the interaction between abusive supervision and a mastery climate is negatively related to knowledge hiding, and the interaction between abusive supervision and a performance climate is positively related to knowledge hiding. On the one hand, when the organization pays more attention to individual performance feedback, performance-prove goal orientation can positively predict knowledge hiding. On the other hand, when the organization pays more attention to group performance feedback, performance-prove goal orientation is negatively correlated with knowledge hiding ( Zhu et al., 2019 ). Compared to individual rewards, team-based rewards are more likely to reduce the distrust caused by knowledge hiding, promoting the team to work hard to achieve a common goal, forming a relatively stable team structure, and improving team viability ( Wang et al., 2019 ). Yao et al. (2020a , b ) reveal the buffering effect of a forgiveness climate on the relationship between negative workplace gossip/workplace bullying and knowledge hiding. Khalid et al. (2018) clarify the role of Islamic work ethics in moderating the relationship between abusive supervision and knowledge hiding. Among these findings, the existing research on the moderating effects still focuses more on the first stage of the antecedents–knowledge hiding–consequences linkage, but there is a lack of systematic development of the moderation mechanism in the second stage.

Future Research Directions

Based on a descriptive analysis, bibliometric analysis, and content analysis, we find that research on knowledge hiding focuses mainly on five clusters. Despite the ongoing progress, several research gaps are worth further addressing.

(1) Comprehensive studies on the concept and dimensions of knowledge hiding are needed to provide a robust conceptual framework. Although the definition and three-dimensional view of knowledge hiding by Connelly et al. (2012) are widely adopted by many scholars, more research is needed to carry out in-depth comparative analysis to clarify the connections and differences between knowledge hiding and similar concepts (e.g., knowledge non-sharing, knowledge sharing hostility, knowledge contribution loafing, counterproductive knowledge behavior, knowledge hoarding, knowledge protection, employee silence, etc.). Further, more studies should continue exploring the dimensions of knowledge hiding. There is a lack of focus on knowledge hiders' psychological motivation and respective knowledge hiding strategies. For example, research on proactive, reactive, and passive knowledge hiding could enrich the field research. In addition, more studies should further explore the unique reasons and consequences of a rationalized hiding behavior. There is a need to verify the ethical aspect of rationalized hiding, when knowledge hiding is used to protect confidential information or the interests of third parties ( Zhao et al., 2019 ).

(2) Future studies need to further explore the consequences of knowledge hiding. Based on a systematic review (see Figure 3 ), we find that previous studies have focused mainly on the antecedents of knowledge hiding. Although some studies have addressed the impacts of knowledge characteristics, individual factors, team-level and interpersonal factors, and organizational-level factors on knowledge hiding, more work is needed to provide comprehensive studies on the generating mechanisms and the respective coping strategies of knowledge hiding. Prior studies have shown that knowledge hiding has impacts on individual-level outcomes (e.g., individual creativity, in-role and extra-role performance, and coworker relationships) and team-level outcomes (e.g., team creativity). However, there is a lack of research on organizational-level outcomes. Moreover, prior studies focus mainly on the impacts of knowledge hiding on the knowledge seekers and the whole team, but seldom has the research discussed the potential effects of knowledge hiding on the knowledge hiders themselves. Therefore, future research should devote more attention to the negative effects of knowledge hiding on the knowledge hiders, the team, and the organization, and also explore the consequences of different dimensions of knowledge hiding. For example, more studies could address the research gap as to whether knowledge hiding may stimulate self-reflection and prompt moral and psychological compensation for the knowledge hiders. To enrich the multilevel mediating and moderating variables, future studies could explore the boundary conditions of knowledge hiding and their respective knowledge management strategies. In short, it is necessary to increase research on the consequences of knowledge hiding to enrich the antecedents–knowledge hiding–consequences research path.

(3) More studies on multilateral, cross-level, and collective knowledge hiding are needed, and it is appropriate to introduce new paradigms for knowledge hiding research. Existing research on knowledge hiding highlights mainly two parties: the hider (A) and the seeker (B) (i.e., B seeks knowledge from A, while A hides knowledge from B). Most studies address knowledge hiding among colleagues at the horizontal level. In recent years, some scholars have started to show interest in knowledge hiding at the vertical level, that is, the top-down knowledge hiding of superiors from subordinates. However, the research on the antecedents and the generating mechanisms of knowledge hiding at the vertical level is still in the stage of exploration. There is a lack of research on bottom-up knowledge hiding (of the subordinates from their superiors). Therefore, it is necessary to study knowledge hiding adopted by people from different hierarchies (e.g., bottom, mid, and high levels) in the organizations, comparing the differences between top-down and bottom-up knowledge hiding, so as to identify regular patterns of cross-level knowledge flow within the organizations. Future research could also examine whether the knowledge hiding of top managers could trigger a trickle-down effect, referring to the fact that the behaviors of the top leaders will affect employees in the formal vertical power chain, given that knowledge hiding can be a multi-participant phenomenon. Therefore, future research could examine the contagious effects of knowledge hiding (e.g., B seeks knowledge from A, but A hides knowledge from B; B then feels lost and hides knowledge from other colleagues), diffusion effects (e.g., B seeks knowledge from A while A hides knowledge from B; A asks C to hide knowledge from B as well), bystander effects (e.g., B seeks knowledge from A, while A hides knowledge from B; C witnesses A's knowledge hiding and is influenced by it, so C also hides knowledge from B and other colleagues), and collective knowledge hiding.

(4) Future scholars should innovate theoretical perspectives and integrate multidisciplinary theories into knowledge hiding research. At present, knowledge hiding research is based mainly on theories such as social exchange, social cognition, social capital, social learning, conservation of resources, territoriality, and psychological ownership. To enrich the field research, it is necessary to diversify the theories. For example, future studies could explore the influence of social exchange relations (e.g., relative LMX) on knowledge hiding, comparing the influence of social LMX and economic LMX on employee willingness to hide knowledge. Future scholars could also conduct multi-interdisciplinary research studies. The research on how an individual's previous workplace behavior affects his or her subsequent workplace behavior has attracted great interest from scholars and mainstream journals in organizational behavior in recent years. Given that knowledge hiding is a typical morality-related behavior, future research could introduce novel and original theoretical viewpoints. For example, a moral balance model and a moral cleansing effect in disciplines such as moral psychology and cognitive psychology, can be used to explore how an individual's previous knowledge hiding behavior influences subsequent behavior in the workplace. Furthermore, knowledge hiding is considered as an emotion-driven behavior. Therefore, scholars could consider employing Lazarus's cognitive–motivational–relational (CMR) theory of emotion ( Lazarus, 1991 ) to better understand the psychological process behind knowledge hiding. Moreover, there is a lack of research on the relationship between individual affect/emotion and knowledge hiding. Therefore, scholars could employ theories, such as affective events theory and self-conscious moral emotion theory, to analyze the subsequent behavior of the hiders and seekers who are driven by affect/emotion.

(5) Research designs need more diversification. Most of the prior studies focus on the individuals, and few research studies focus on both individual and team effects. Knowledge hiding is a complex organizational behavior that concerns individual, team/interpersonal, and organizational levels. Therefore, future research could introduce data tracking technologies, such as big data analysis, to study and compare the dynamic and static (long-term and short-term) effects of multilevel knowledge hiding. Moreover, it is necessary to diversify research methods in the field. Most existing research uses one-wave or multistage surveys, employee self-evaluation, and empirical tests, with few studies using case studies and interviews. These research methods may suffer from a lack of reliability of data sources. Future research could integrate multiple methodologies (e.g., combining case studies, experimental research, surveys, and objective data mining) to verify data, which could improve the internal and external validity of the research and enhance the robustness of conclusions. In particular, it is necessary to focus on the combination of experimental and empirical research, making full use of the strengths of each method to validate the research. Researchers could carry out preliminary tests on relevant hypotheses through experimental research and then supplement them with surveys for secondary verification.

(6) Future research should integrate more cultural, sectoral, and organizational factors to enrich the findings. As discussed in the findings, most of the knowledge hiding data were collected in China and Pakistan. It is necessary to develop the diversity of knowledge hiding data in terms of country of origin. In addition, there is a lack of cross-country academic collaboration. Collaborating across borders could help to generate new ideas and allow for collecting data from different sources. Meanwhile, it would be very interesting to promote cross-country studies to identify the different definitions, perceptions, implementations, and patterns of knowledge hiding, whilst paying more attention to the relationship between cultural dimensions and knowledge hiding. Apart from cross-cultural and cross-country variables, future research could also investigate industry characteristics (such as knowledge-intensive and non-knowledge-intensive industries and masculine and feminine industries), team standards/norms (such as team moral norms), and firm size (small medium enterprises vs. multinational companies) so as to identify the boundary conditions of individual knowledge hiding behavior. Through conducting sector-specific and cross-sector comparison for knowledge hiding, we would be able to adjust knowledge management methods.

Conclusions

This article provides a systematic review of knowledge hiding. It contributes to the identification of publication patterns on knowledge hiding between 2012 and 2020. Further, we have highlighted the most influential studies, mapped the research gaps, and provided the potential research directions in the field.

This study is not without limitations. We use SCI and SSCI web of science as the databases. Using this literature search method excludes book chapters, reports, unpublished dissertations, with/without peer reviewed conference proceedings, newsletters, government documents, and working papers. Consequently, this review may not have captured the full range of scholarly literature on knowledge hiding. In the future, to reduce the publication bias ( Kepes et al., 2012 ), it would be interesting to include other databases to search literatures, for instance, the work published in the Emerging Sources Citation Index (ESCI) journals can be considered. Second, the research on knowledge hiding is emerging, and some scholars may argue that it is not yet mature enough to review the research field. In our opinion, it is only with such a complete literature review that a clear picture of knowledge hiding research can be developed so that scholars can better define research problems, innovate the research theories and methods, and enrich the field research with a robust framework.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

PH, CJ, ZX, and CS designed and supervised the study. PH collected the data. PH and ZX analyzed the data. PH, CJ, and CS wrote the manuscript. All authors contributed equally to this manuscript, reviewed, and approved this manuscript for publication.

Funding was provided by Huaqiao University's Academic Project Supported by the Fundamental Research Funds for the Central Universities (20SKGC-QT02) and the National Natural Science Foundation of China (72172048).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

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.

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Keywords: knowledge hiding, systematic literature review, future research directions, content analysis, bibliometric analysis, descriptive analysis

Citation: He P, Jiang C, Xu Z and Shen C (2021) Knowledge Hiding: Current Research Status and Future Research Directions. Front. Psychol. 12:748237. doi: 10.3389/fpsyg.2021.748237

Received: 27 July 2021; Accepted: 05 October 2021; Published: 29 October 2021.

Reviewed by:

Copyright © 2021 He, Jiang, Xu and Shen. 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) and the copyright owner(s) 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: Zhixing Xu, xuzhixing@bnu.edu.cn ; Chuangang Shen, psychshen@hqu.edu.cn

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.

Pharmacokinetics and Drug Interactions in the Elderly and Special Issues in Elderly African-American Populations: Workshop Summary (1997)

Chapter: 3 conclusions and future research directions, conclusions and future research directions.

The ongoing study of pharmacokinetics, pharmacodynamics, and drug interactions in elderly persons is critical for the development of safe and effective therapies and for the prevention of drug toxicities and adverse drug reactions. Aging is associated with an increase in chronic illness and anatomical and physiological changes that affect drug distribution, metabolism, and excretion. Thus, as the number of older Americans increases, it can be expected that polypharmacy in this population will have significant health, social, and economic consequences. Additionally, research should focus on alleviating the disease burden in elderly minority populations.

Box 3.1 summarizes the committee's conclusions regarding future directions for research in this field. The remainder of this chapter provides a more detailed discussion of the committee's conclusions.

RESEARCH NEEDS AND OPPORTUNITIES

Expanding the scientific knowledge base.

Although progress has been made in understanding the aging process, there is still a paucity of data at the intracellular, organ, system, and population levels. The impact of aging on cells and organ systems has commonly been studied in isolation; however, a more integrated approach is needed that will examine the effects of aging on the body. Pharmacokinetic and pharmacodynamic models need to be developed that encompass the entire range of changes occurring at multiple levels throughout the body.

The following list highlights specific areas of research that would add to the body of knowledge and clarify our understanding of the aging process especially with regard to improving pharmacotherapy. This list is by no means comprehensive, as numerous research avenues could yield important information on the impact of pharmacotherapy and drug interactions in the elderly. Areas for future research include the impact of aging, gender, genetics, and ethnicity on physiology and metabolic processes. Specifically,

age-related changes in cellular transport mechanisms and extrahepatic metabolism and transport including the activity of different enzyme isoforms;

biomarkers of drug exposure;

mechanisms that cause variable responses to medications in aging racial and ethnic populations;

age-related hormonal changes affecting drug metabolism or drug sensitivity;

the impact of nutrition on the aging process;

mechanisms underlying diseases prevalent in the elderly (e.g., hypertension, diabetes, osteoporosis, and Parkinson's and Alzheimer's disease);

in vitro models for multiple drug regimens and multiple drug interactions that may be predictive of and correlated with in vivo research;

models for drug interaction related to altered reflex activity and changing homeostatic mechanisms;

the potential beneficial and adverse health effects of nutraceuticals; and

social and psychological aspects of medication use in the elderly (e.g., access to medications, adherence to prescription regimens), with a special emphasis on minority populations.

Addressing Issues in Minority Populations

Many diseases are disproportionately prevalent in elderly African-American and other minority populations. The causes and implications of this excess burden need to be more completely understood and addressed. For example, hypertension and the impact of antihypertensive medications in elderly African Americans have not been fully studied even though the morbidity and mortality is higher in this population than in other segments of the aging population.

Research is needed on multiple levels (molecular, cellular, system, population) to clarify the effect of race and ethnicity on disease prevalence and on variations in the effectiveness of pharmacotherapeutic and other treatment interventions. Such research would be a valuable tool in increasing our understanding of the physiology of aging for all populations and may have implications for pharmacotherapies aimed at various elderly groups. Research on diseases and health conditions that primarily affect minority elderly populations needs to be a priority to alleviate the disease burden experienced by these populations.

Recruitment of Elderly Patients into Clinical Trials

In 1989, the FDA published a guideline for the inclusion of elderly persons in clinical trials (FDA, 1989). However, a number of characteristics of the elderly population may present barriers to conducting clinical trials that are representative of this population. Studies need to include the oldest segment of the population (see Chapter 2 ). In addition, subgroups of the elderly population should be stratified based primarily on their functional status and disease burden and less on their chronological age.

Recruiting minorities for inclusion in studies should be a priority, although it is important to recognize the trends toward multiracial backgrounds and the complexities associated with categorizing race or ethnicity. The workshop speakers presented many innovative ideas about increasing the recruitment of minority populations. The committee supports a number of approaches, including providing transportation, involving the minority community, providing extensive patient education, and decentralizing clinical trials (i.e.., going to patients' homes or to community centers to provide and assess treatment). In addition, collaborative efforts and consortia need to be strengthened between historically minority and other academic institutions. These partnerships will be vital to recruiting minority investigators and to attracting and sustaining minority students in research programs. Further, patient recruitment efforts can draw on the populations available to both institutions.

Obtaining informed consent in elderly populations involves complex issues that need to be addressed including the extent of dementia or cognitive impairment in some elderly patients and their vulnerability to coercion. Informed consent forms have evolved into highly technical legal documents, and a reevaluation of how to best meet their original purpose is needed. Other ethical issues that need to be addressed include studies on vulnerable populations (e.g., nursing home residents) and the confidentiality of patient information.

Research Methodologies and Tools

Trials of acute drug use are well funded; however, there are few long-term studies that examine chronic effects and drug interactions. Inasmuch as elderly persons are living longer and may take the same medications for many years, increased postmarketing surveillance is needed to examine the effects of long-term use of drugs. Incentives to strengthen postmarketing surveillance should be considered. Some of these drugs (e.g., hormone replacement therapy, antidepressants, and lipid-lowering medications) may be used as preventive measures (e.g., treating high cholesterol levels in the absence of cardiovascular disease or prescribing hormone replacement therapy to prevent hip fractures); however, their long-term health effects are not fully known. Further, the pharmacodynamics of many of these medications are only beginning to be investigated.

Research Methodologies

Studying the impact of pharmacotherapy on the elderly population is often difficult from a methodological standpoint. Cross-sectional studies are problematic because confounding variables abound among the elderly, and it is difficult to distinguish the effects of aging from those of disease. Randomized clini-

cal trials often recruit study subjects who represent the younger segments of the elderly population, and who have fewer comorbid conditions, use fewer medications, and may be more compliant in terms of following prescription medication regimens. In addition, many studies use small numbers of patients, frequently with homogenous geographic and ethnic backgrounds. Longitudinal studies are needed that involve large numbers of patients who reflect the diversity of “real world” populations. Furthermore, studies of elderly populations should include observational studies, case-control studies, and cohort studies to take advantage of the realm of methodological approaches that are available. Studies of optimal pharmacotherapies need to consider their cost-effectiveness and delivery. Outcome measures must also be reexamined, and quality-of-life outcomes need to be considered. One workshop speaker recalled the adage that, “adding life to years is at least as important as adding years to life.”

Databases Available for Research

There is a notable lack of adequate databases to research the prevalence and health impact of adverse drug reactions in elderly populations. Prescription information on elderly persons is not currently linked to diagnostic information or health outcomes data. For example, state Medicaid databases are used to reimburse pharmacies for prescriptions, therefore, the data on medication utilization are quite complete and accurate. However, diagnostic information for outpatient care is often incomplete or unavailable, and is not linked to pharmacy utilization databases.

Current changes underway in the health care delivery system may provide opportunities for new databases to be developed, although there are concerns that these changes may instead result in the loss of publicly available data. Trends of interest include the purchase of pharmacy benefit companies by pharmaceutical manufacturers and the increased use of managed care through proprietary health plans paid for by Medicaid and Medicare. Increased use of managed care to provide health care for the elderly offers opportunities for databases to be implemented that would link health outcomes (particularly adverse drug reactions) and prescription information, while paying close attention to patient confidentiality issues. However, these changes may instead be implemented to restructure datasets and require new levels of approval for data use or publishing. It is crucial that the larger issues involving potential censoring or loss of publicly available data on prescription drug use and health outcomes be addressed. The increased privatization of health care services for the elderly may lead to barriers to accessing datasets due to proprietary and competitive interests.

An area of interest to the committee is exploring the feasibility of developing a cooperative national data resource that would expand the researcher's

ability to examine population-based data rather than utilizing a piecemeal approach to data collection. This data resource could include information on diagnosis, medications prescribed, clinical interventions, health outcomes, and other relevant data. Of utmost importance would be maintaining patient confidentiality. This resource could be utilized as a repository to which individual researchers could submit peer-reviewed and approved research questions. Examining the feasibility of developing such a data resource would require the input of patients, health care providers, researchers, ethicists, and other interested persons and groups.

Dissemination of Information

Drug-related information can be complex, and information overload is a common phenomenon among health professionals and patients. Because information regarding drug use and interactions changes rapidly, information systems should be available to health care professionals that can provide up-to-date information that is unbiased, case specific, interactive, and readily accessible. In addition, it is important to develop diverse information dissemination strategies to effectively meet the needs of the heterogeneous elderly population. The information provided should be presented in a manner that can be understood by patients who cannot necessarily process complex information, yet need to make informed decisions and understand their options for treatment. Private- and public-sector initiatives are required to address this critical challenge. Health sciences centers that focus on training medical, nursing, and pharmacy students should be used to conduct independent, unbiased research and continuing education programs for practicing physicians, pharmacists, nurses, and the interested public (Woosley, 1994).

Capacity Building: Researchers and Clinicians

One of the major factors limiting the expansion of research in the area of geriatrics, and particularly geriatric pharmacology and clinical therapeutics, is the small number of health professionals entering this field. This is an area in which, as demographers can attest, the patient base is expanding and will continue to grow. Quality geriatric care depends on the development of multidisciplinary teams (including nursing, physical and occupational therapy, social work) to assess the concomitant problems and implement multiple interventions. However, reimbursements do not adequately cover the time required to handle the complexity of geriatric care. A recent report by the Alliance for Aging Research (1996) found that the United States has less than one fourth the number

of academic physician-scientists needed in geriatrics to teach and conduct research.

There is a pressing need to develop innovative approaches for recruiting and retaining researchers and clinicians. Programs are needed at many points along the career path, beginning early in the college and postbaccalaureate years to kindle an interest in the field of geriatrics and continuing throughout the professional years to retain the best investigators and clinicians available. Recruitment of minority investigators should be included within broader programs supporting young and mid-career investigators. The options available for approaching this issue include

1- to 2-year postbaccalaureate programs to provide research and clinical experience to young people considering a career in this field;

opportunities for medical, nursing, pharmacy, and other health professional students to have additional exposure to geriatric treatment and research during their education;

collaborative efforts between minority academic institutions and academic health sciences centers to encourage minority students to pursue a research program in this field;

loan-forgiveness programs to assist young researchers with high debt loads from health professional or graduate schools;

new sources of fellowships (e.g., through FDA, pharmaceutical companies, or insurance companies);

increased commitment to funding from fellowship training to first awards to independent grant support;

merit awards at the midcareer level and specialized sabbaticals to retrain midcareer professionals; and

retraining in research methodologies during sabbaticals for midcareer level geriatricians.

Currently there is only a limited understanding of the impact of aging on pharmacokinetics, pharmacodynamics, and drug interactions. Research is needed at the molecular, cellular, organ, system, and population levels for safer and more effective medications to be developed, delivered, and utilized by elderly persons. In addition, attention must be given to understanding and alleviating the disproportionate disease burden in elderly African-American and other minority populations.

The committee's major conclusions are summarized in Box 3.1 at the beginning of this chapter. The committee discussed and reflected only on the workshop presentations and acknowledges that there are numerous research

opportunities in geriatric pharmacology that need to be explored. Research in geriatric pharmacology and clinical therapeutics will require a commitment to fund studies that can further elucidate the relationship between pharmacokinetics and adverse drug interactions in the elderly and the complex individual variability of the aging process. Increasing the knowledge base will enable more effective therapeutic interventions and improved quality of life for the growing population of elderly persons.

Alliance for Aging Research. 1996. Will You Still Treat Me When I'm 65? The National Shortage of Geriatricians . Washington, DC: Alliance for Aging Research.

FDA (Food and Drug Administration). 1989. Guideline for the Study of Drugs Likely to Be Used in the Elderly. Rockville, MD: FDA Center for Drug Evaluation and Research.

Woosley RL. 1994. Centers for education and research in therapeutics. Clinical Pharmacology and Therapeutics 56(6 Part 1):693–697.

Reports in the popular press about the increasing longevity of Americans and the aging of the baby boom generation are constant reminders that the American population is becoming older. Consequently, an issue of growing medical, health policy, and social concern is the appropriate and rational use of medications by the elderly.

Although becoming older does not necessarily correlate with increasing illness, aging is associated with anatomical and physiological changes that affect how medications are metabolized by the body. Furthermore, aging is often related to an increased frequency of chronic illness (often combined with multiple health problems) and an increased use of medications. Thus, a better understanding of the absorption, distribution, metabolism, and excretion of drugs; of the physiologic responses to those medications; as well as of the interactions among multiple medications is crucial for improving the health of older people.

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Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.)

Public involvement in research: assessing impact through a realist evaluation.

Chapter 9 conclusions and recommendations for future research.

• How well have we achieved our original aim and objectives?

The initially stated overarching aim of this research was to identify the contextual factors and mechanisms that are regularly associated with effective and cost-effective public involvement in research. While recognising the limitations of our analysis, we believe we have largely achieved this in our revised theory of public involvement in research set out in Chapter 8 . We have developed and tested this theory of public involvement in research in eight diverse case studies; this has highlighted important contextual factors, in particular PI leadership, which had not previously been prominent in the literature. We have identified how this critical contextual factor shapes key mechanisms of public involvement, including the identification of a senior lead for involvement, resource allocation for involvement and facilitation of research partners. These mechanisms then lead to specific outcomes in improving the quality of research, notably recruitment strategies and materials and data collection tools and methods. We have identified a ‘virtuous circle’ of feedback to research partners on their contribution leading to their improved confidence and motivation, which facilitates their continued contribution. Following feedback from the HS&DR Board on our original application we did not seek to assess the cost-effectiveness of different mechanisms of public involvement but we did cost the different types of public involvement as discussed in Chapter 7 . A key finding is that many research projects undercost public involvement.

In our original proposal we emphasised our desire to include case studies involving young people and families with children in the research process. We recruited two studies involving parents of young children aged under 5 years, and two projects involving ‘older’ young people in the 18- to 25-years age group. We recognise that in doing this we missed studies involving children and young people aged under 18 years; in principle we would have liked to have included studies involving such children and young people, but, given the resources at our disposal and the additional resource, ethical and governance issues this would have entailed, we regretfully concluded that this would not be feasible for our study. In terms of the four studies with parental and young persons’ involvement that we did include, we have not done a separate analysis of their data, but the themes emerging from those case studies were consistent with our other case studies and contributed to our overall analysis.

In terms of the initial objectives, we successfully recruited the sample of eight diverse case studies and collected and analysed data from them (objective 1). As intended, we identified the outcomes of involvement from multiple stakeholders‘ perspectives, although we did not get as many research partners‘ perspectives as we would have liked – see limitations below (objective 2). It was more difficult than expected to track the impact of public involvement from project inception through to completion (objective 3), as all of our projects turned out to have longer time scales than our own. Even to track involvement over a stage of a case study research project proved difficult, as the research usually did not fall into neatly staged time periods and one study had no involvement activity over the study period.

Nevertheless, we were able to track seven of the eight case studies prospectively and in real time over time periods of up to 9 months, giving us an unusual window on involvement processes that have previously mainly been observed retrospectively. We were successful in comparing the contextual factors, mechanisms and outcomes associated with public involvement from different stakeholders‘ perspectives and costing the different mechanisms for public involvement (objective 4). We only partly achieved our final objective of undertaking a consensus exercise among stakeholders to assess the merits of the realist evaluation approach and our approach to the measurement and valuation of economic costs of public involvement in research (objective 5). A final consensus event was held, where very useful discussion and amendment of our theory of public involvement took place, and the economic approach was discussed and helpfully critiqued by participants. However, as our earlier discussions developed more fully than expected, we decided to let them continue rather than interrupt them in order to run the final exercise to assess the merits of the realist evaluation approach. We did, however, test our analysis with all our case study participants by sending a draft of this final report for comment. We received a number of helpful comments and corrections but no disagreement with our overall analysis.

• What were the limitations of our study?

Realist evaluation is a relatively new approach and we recognise that there were a number of limitations to our study. We sought to follow the approach recommended by Pawson, but we acknowledge that we were not always able to do so. In particular, our theory of public involvement in research evolved over time and initially was not as tightly framed in terms of a testable hypothesis as Pawson recommends. In his latest book Pawson strongly recommends that outcomes should be measured with quantitative data, 17 but we did not do so; we were not aware of the existence of quantitative data or tools that would enable us to collect such data to answer our research questions. Even in terms of qualitative data, we did not capture as much information on outcomes as we initially envisaged. There were several reasons for this. The most important was that capturing outcomes in public involvement is easier the more operational the focus of involvement, and more difficult the more strategic the involvement. Thus, it was relatively easy to see the impact of a patient panel on the redesign of a recruitment leaflet but harder to capture the impact of research partners in a multidisciplinary team discussion of research design.

We also found it was sometimes more difficult to engage research partners as participants in our research than researchers or research managers. On reflection this is not surprising. Research partners are generally motivated to take part in research relevant to their lived experience of a health condition or situation, whereas our research was quite detached from their lived experience; in addition people had many constraints on their time, so getting involved in our research as well as their own was likely to be a burden too far for some. Researchers clearly also face significant time pressures but they had a more direct interest in our research, as they are obliged to engage with public involvement to satisfy research funders such as the NIHR. Moreover, researchers were being paid by their employers for their time during interviews with us, while research partners were not paid by us and usually not paid by their research teams. Whatever the reasons, we had less response from research partners than researchers or research managers, particularly for the third round of data collection; thus we have fewer data on outcomes from research partners‘ perspectives and we need to be aware of a possible selection bias towards more engaged research partners. Such a bias could have implications for our findings; for example payment might have been a more important motivating factor for less engaged advisory group members.

There were a number of practical difficulties we encountered. One challenge was when to recruit the case studies. We recruited four of our eight case studies prior to the full application, but this was more than 1 year before our project started and 15 months or more before data collection began. In this intervening period, we found that the time scales of some of the case studies were no longer ideal for our project and we faced the choice of whether to continue with them, although this timing was not ideal, or seek at a late moment to recruit alternative ones. One of our case studies ultimately undertook no involvement activity over the study period, so we obtained fewer data from it, and it contributed relatively little to our analysis. Similarly, one of the four case studies we recruited later experienced some delays itself in beginning and so we had a more limited period for data collection than initially envisaged. Research governance approvals took much longer than expected, particularly as we had to take three of our research partners, who were going to collect data within NHS projects, through the research passport process, which essentially truncated our data collection period from 1 year to 9 months. Even if we had had the full year initially envisaged for data collection, our conclusion with hindsight was that this was insufficiently long. To compare initial plans and intentions for involvement with the reality of what actually happened required a longer time period than a year for most of our case studies.

In the light of the importance we have placed on the commitment of PIs, there is an issue of potential selection bias in the recruitment of our sample. As our sampling strategy explicitly involved a networking approach to PIs of projects where we thought some significant public involvement was taking place, we were likely (as we did) to recruit enthusiasts and, at worst, those non-committed who were at least open to the potential value of public involvement. There were, unsurprisingly, no highly sceptical PIs in our sample. We have no data therefore on how public involvement may work in research where the PI is sceptical but may feel compelled to undertake involvement because of funder requirements or other factors.

• What would we do differently next time?

If we were to design this study again, there are a number of changes we would make. Most importantly we would go for a longer time period to be able to capture involvement through the whole research process from initial design through to dissemination. We would seek to recruit far more potential case studies in principle, so that we had greater choice of which to proceed with once our study began in earnest. We would include case studies from the application stage to capture the important early involvement of research partners in the initial design period. It might be preferable to research a smaller number of case studies, allowing a more in-depth ethnographic approach. Although challenging, it would be very informative to seek to sample sceptical PIs. This might require a brief screening exercise of a larger group of PIs on their attitudes to and experience of public involvement.

The economic evaluation was challenging in a number of ways, particularly in seeking to obtain completed resource logs from case study research partners. Having a 2-week data collection period was also problematic in a field such as public involvement, where activity may be very episodic and infrequent. Thus, collecting economic data alongside other case study data in a more integrated way, and particularly with interviews and more ethnographic observation of case study activities, might be advantageous. The new budgeting tool developed by INVOLVE and the MHRN may provide a useful resource for future economic evaluations. 23

We have learned much from the involvement of research partners in our research team and, although many aspects of our approach worked well, there are some things we would do differently in future. Even though we included substantial resources for research partner involvement in all aspects of our study, we underestimated how time-consuming such full involvement would be. We were perhaps overambitious in trying to ensure such full involvement with the number of research partners and the number and complexity of the case studies. We were also perhaps naive in expecting all the research partners to play the same role in the team; different research partners came with different experiences and skills, and, like most of our case studies, we might have been better to be less prescriptive and allow the roles to develop more organically within the project.

• Implications for research practice and funding

If one of the objectives of R&D policy is to increase the extent and effectiveness of public involvement in research, then a key implication of this research is the importance of influencing PIs to value public involvement in research or to delegate to other senior colleagues in leading on involvement in their research. Training is unlikely to be the key mechanism here; senior researchers are much more likely to be influenced by peers or by their personal experience of the benefits of public involvement. Early career researchers may be shaped by training but again peer learning and culture may be more influential. For those researchers sceptical or agnostic about public involvement, the requirement of funders is a key factor that is likely to make them engage with the involvement agenda. Therefore, funders need to scrutinise the track record of research teams on public involvement to ascertain whether there is any evidence of commitment or leadership on involvement.

One of the findings of the economic analysis was that PIs have consistently underestimated the costs of public involvement in their grant applications. Clearly the field will benefit from the guidance and budgeting tool recently disseminated by MHRN and INVOLVE. It was also notable that there was a degree of variation in the real costs of public involvement and that effective involvement is not necessarily costly. Different models of involvement incur different costs and researchers need to be made aware of the costs and benefits of these different options.

One methodological lesson we learned was the impact that conducting this research had on some participants’ reflection on the impact of public involvement. Particularly for research staff, the questions we asked sometimes made them reflect upon what they were doing and change aspects of their approach to involvement. Thus, the more the NIHR and other funders can build reporting, audit and other forms of evaluation on the impact of public involvement directly into their processes with PIs, the more likely such questioning might stimulate similar reflection.

• Recommendations for further research

There are a number of gaps in our knowledge around public involvement in research that follow from our findings, and would benefit from further research, including realist evaluation to extend and further test the theory we have developed here:

• In-depth exploration of how PIs become committed to public involvement and how to influence agnostic or sceptical PIs would be very helpful. Further research might compare, for example, training with peer-influencing strategies in engendering PI commitment. Research could explore the leadership role of other research team members, including research partners, and how collective leadership might support effective public involvement.
• More methodological work is needed on how to robustly capture the impact and outcomes of public involvement in research (building as well on the PiiAF work of Popay et al. 51 ), including further economic analysis and exploration of impact when research partners are integral to research teams.
• Research to develop approaches and carry out a full cost–benefit analysis of public involvement in research would be beneficial. Although methodologically challenging, it would be very useful to conduct some longer-term studies which sought to quantify the impact of public involvement on such key indicators as participant recruitment and retention in clinical trials.
• It would also be helpful to capture qualitatively the experiences and perspectives of research partners who have had mixed or negative experiences, since they may be less likely than enthusiasts to volunteer to participate in studies of involvement in research such as ours. Similarly, further research might explore the (relatively rare) experiences of marginalised and seldom-heard groups involved in research.
• Payment for public involvement in research remains a contested issue with strongly held positions for and against; it would be helpful to further explore the value research partners and researchers place on payment and its effectiveness for enhancing involvement in and impact on research.
• A final relatively narrow but important question that we identified after data collection had finished is: what is the impact of the long periods of relative non-involvement following initial periods of more intense involvement for research partners in some types of research, particularly clinical trials?

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• Cite this Page Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.) Chapter 9, Conclusions and recommendations for future research.
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The future of research: Emerging trends and new directions in scientific inquiry

The world of research is constantly evolving, and staying on top of emerging trends is crucial for advancing scientific inquiry. With the rapid development of technology and the increasing focus on interdisciplinary research, the future of research is filled with exciting opportunities and new directions.

In this article, we will explore the future of research, including emerging trends and new directions in scientific inquiry. We will examine the impact of technological advancements, interdisciplinary research, and other factors that are shaping the future of research.

One of the most significant trends shaping the future of research is the rapid development of technology. From big data analytics to machine learning and artificial intelligence, technology is changing the way we conduct research and opening up new avenues for scientific inquiry. With the ability to process vast amounts of data in real-time, researchers can gain insights into complex problems that were once impossible to solve.

Another important trend in the future of research is the increasing focus on interdisciplinary research. As the boundaries between different fields of study become more fluid, interdisciplinary research is becoming essential for addressing complex problems that require diverse perspectives and expertise. By combining the insights and methods of different fields, researchers can generate new insights and solutions that would not be possible with a single-discipline approach.

One emerging trend in research is the use of virtual and augmented reality (VR/AR) to enhance scientific inquiry. VR/AR technologies have the potential to transform the way we conduct experiments, visualize data, and collaborate with other researchers. For example, VR/AR simulations can allow researchers to explore complex data sets in three dimensions, enabling them to identify patterns and relationships that would be difficult to discern in two-dimensional representations.

Another emerging trend in research is the use of open science practices. Open science involves making research data, methods, and findings freely available to the public, facilitating collaboration and transparency in the scientific community. Open science practices can help to accelerate the pace of research by enabling researchers to build on each other’s work more easily and reducing the potential for duplication of effort.

The future of research is also marked by scientific innovation, with new technologies and approaches being developed to address some of the world’s most pressing problems. For example, gene editing technologies like CRISPR-Cas9 have the potential to revolutionize medicine by allowing scientists to edit DNA and cure genetic diseases. Similarly, nanotechnology has the potential to create new materials with unprecedented properties, leading to advances in fields like energy, electronics, and medicine.

One new direction in research is the focus on sustainability and the environment. With climate change and other environmental issues becoming increasingly urgent, researchers are turning their attention to developing sustainable solutions to the world’s problems. This includes everything from developing new materials and technologies to reduce greenhouse gas emissions to developing sustainable agricultural practices that can feed the world’s growing population without damaging the environment.

Another new direction in research is the focus on mental health and wellbeing. With mental health issues becoming increasingly prevalent, researchers are exploring new approaches to understanding and treating mental illness. This includes everything from developing new therapies and medications to exploring the role of lifestyle factors like diet, exercise, and sleep in mental health.

In conclusion, the future of research is filled with exciting opportunities and new directions. By staying on top of emerging trends, embracing interdisciplinary research, and harnessing the power of technological innovation, researchers can make significant contributions to scientific inquiry and address some of the world’s most pressing problems.

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Suggestions for Future Research

Your dissertation needs to include suggestions for future research. Depending on requirements of your university, suggestions for future research can be either integrated into Research Limitations section or it can be a separate section.

You will need to propose 4-5 suggestions for future studies and these can include the following:

1. Building upon findings of your research . These may relate to findings of your study that you did not anticipate. Moreover, you may suggest future research to address unanswered aspects of your research problem.

2. Addressing limitations of your research . Your research will not be free from limitations and these may relate to formulation of research aim and objectives, application of data collection method, sample size, scope of discussions and analysis etc. You can propose future research suggestions that address the limitations of your study.

3. Constructing the same research in a new context, location and/or culture . It is most likely that you have addressed your research problem within the settings of specific context, location and/or culture. Accordingly, you can propose future studies that can address the same research problem in a different settings, context, location and/or culture.

4. Re-assessing and expanding theory, framework or model you have addressed in your research . Future studies can address the effects of specific event, emergence of a new theory or evidence and/or other recent phenomenon on your research problem.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

Bibliometric review of research on economic complexity: current trends, developments, and future research directions

• Published: 25 April 2024

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• Behrooz Shahmoradi 1 &
• Nejla Ould Daoud Ellili 2  

The growing literature on the issues of economic complexity makes it challenging to achieve a comprehensive multidimensional picture of the current problem for beneficiaries, policymakers, and future research. Therefore, this study aims to conduct a bibliometric analysis of 272 documents published in the field of economic complexity since 2007 and extracted from the Scopus database. Results are presented through figures, tables, maps of past trends and research directions using keyword analysis, global citation analysis of authors, organizations, countries, journals, articles, references, content analysis, and other bibliometric analysis via VOSviewer, CiteSpace, and WordStat software. A bibliometric review was applied to identify four clusters: Economic Growth, Diversification, Income Inequality, and Ecological Footprint. Finally, the state of the art in economic complexity research is discussed, and directions for future research are provided.

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Shahmoradi, B., Ellili, N.O.D. Bibliometric review of research on economic complexity: current trends, developments, and future research directions. J. Ind. Bus. Econ. (2024). https://doi.org/10.1007/s40812-024-00298-0

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Compound dry and hot extremes: A review and future research pathways for India

• Guntu, Ravi Kumar
• Agarwal, Ankit

Compound Dry and Hot Extremes (CDHEs) is gaining attention compared to individual dry or hot extremes, due to their amplified impacts on both the population and ecosystems in India. This underscores the importance of transitioning from studying individual extremes to adopting a compound perspective. Despite this, investigation of CDHEs during the Indian summer monsoon remain limited, and a comprehensive review of methodologies for the investigation of CDHE is absent. This review systematically synthesizes recent literature, covering concepts of CDHE with illustrative examples, including identification, characterization, drivers, and prediction. It illustrates three widely used methods for the identification of CDHEs along with their advantages and disadvantages. Furthermore, it describes concepts with illustrative examples to investigate the characteristics (frequency, spatial extent, timing, duration, severity, and likelihood), explores drivers using event coincidence analysis and a complexity-based framework, and discusses the strengths and weaknesses of a logistic regression model for predicting the occurrence of CDHE. In light of the growing significance of CDHEs, we suggest future directions for Indian CDHE research, including an improved characterization of CDHEs across multiple temporal and spatial scales, a deep understanding of the physical mechanism, a robust evaluation of climate models, attribution and projection, and a comprehensive impact assessment. CDHEs are the new normal, and there is an urgent need to advance research on CDHEs in vulnerable regions like India to combat and mitigate their effects.

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Researchers create skyrmion-based memory technology for extremely low-power devices

A research team led by the Agency for Science, Technology and Research (A*STAR) in partnership with National University of Singapore (NUS) has created an innovative microelectronic device that can potentially function as a sustainable, high-performance "bit-switch." This paves the way for future computing technologies to process data much faster while using significantly less energy.

By harnessing tiny, stable and speedy magnetic whirls called skyrmions, the device can operate using 1,000 times less power than commercial memory technologies. This discovery was reported in the journal Nature .

The need for more sustainable and efficient AI computing

Emerging AI technologies such as ChatGPT require large amounts of data to be processed at blazing speeds, which draw on immense computing power. Infocomm technologies already consume nearly 20% of global electricity, which is set to spike even further with the growth of such large AI models. To meet these rapidly growing demands, the fundamental computing "switch," or memory-bit , has been scaled down to ever-smaller sizes, and is approaching its physical limits.

A prominent approach to mitigate this energy crisis, especially for mobility, health care and manufacturing domains, is edge computing. Here, data is processed within individual appliances, such as phones, smart home appliances and vehicles, rather than in power-intensive large-scale data centers. However, edge appliances are presently unable to perform complex computational tasks due to limited computing capacity and power constraints. There is a pressing need to develop a radically different microelectronic platform in order to achieve efficient and sustainable AI computing.

Activating the potential of skyrmions

Skyrmions are tiny magnetic whirls—10,000 times smaller than the width of a human hair—that form within specific magnetic layers when they are made extremely thin. Discovered only a decade ago, these whirls can be extremely stable and compact, and can be efficiently moved between magnetic regions. They form ideal mobile switches for efficient, large-scale data processing for AI technologies.

To tap on the vast potential of skyrmions, it is critical to access them using electrical pathways such as those employed in computers. While skyrmions can be seen under special microscopes and have been manipulated using bulky magnets for over a decade, the absence of electrical control has been a critical impediment to their technological relevance.

The team's breakthrough is the first to achieve electrical readout ("identification") of a skyrmion and electrical switching between states (e.g., "0" to "1," and vice versa). To do so, the team employed a device known as a tunnel junction, which is operable under ambient conditions and used extensively in commercial memory and hard disk applications.

They discovered that the special attributes of skyrmions enable the switching between states using 1,000 times less power than commercial devices. They also found that more than two states can be achieved within a single device, which circumvents the need to scale down the device size for enhanced performance.

Future directions

"Skyrmions have unique and elusive attributes that can be exploited to implement various AI architectures with unprecedented efficiency and functionality. Our microelectronic device provides the long-awaited key to unlocking their vast potential. It will help cement skyrmions as an integral component for the future of computing," said the team leader Dr. Anjan Soumyanarayanan, Principal Scientist at A*STAR's Institute of Materials Research and Engineering (IMRE) and Assistant Professor at the NUS Faculty of Science's Department of Physics.

"Our microelectronic devices are fabricated on 200 mm silicon wafers using materials and methods readily employed in existing microelectronic foundries in Singapore and globally. We hope to collaborate with the electronics cluster ecosystem to accelerate the practical integration of these devices with existing edge computing technologies," said Dr. James Lourembam, Senior Scientist at IMRE's Electronic Materials Department.

The team hopes that with further refinement of the electrical performance, the enhanced computing switch can be readily integrated into microprocessors using established approaches. The team is looking to collaborate with semiconductor manufacturing companies and system integrators to scale up the technology for wider adoption.

More information: Shaohai Chen et al, All-electrical skyrmionic magnetic tunnel junction, Nature (2024). DOI: 10.1038/s41586-024-07131-7

Provided by National University of Singapore