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Internet marketing: a content analysis of the research

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• Published: 31 January 2013
• Volume 23 , pages 177–204, ( 2013 )

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• J. Ken Corley II 1 ,
• Zack Jourdan 2 &
• W. Rhea Ingram 2  

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The amount of research related to Internet marketing has grown rapidly since the dawn of the Internet Age. A review of the literature base will help identify the topics that have been explored as well as identify topics for further research. This research project collects, synthesizes, and analyses both the research strategies (i.e., methodologies) and content (e.g., topics, focus, categories) of the current literature, and then discusses an agenda for future research efforts. We analyzed 411 articles published over the past eighteen years (1994-present) in thirty top Information Systems (IS) journals and 22 articles in the top 5 Marketing journals. The results indicate an increasing level of activity during the 18-year period, a biased distribution of Internet marketing articles focused on exploratory methodologies, and several research strategies that were either underrepresented or absent from the pool of Internet marketing research. We also identified several subject areas that need further exploration. The compilation of the methodologies used and Internet marketing topics being studied can serve to motivate researchers to strengthen current research and explore new areas of this research.

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Introduction

In the early years of the Internet Age, the potential of using the Internet as a distribution channel excited business managers who believed this tool would boost sales and increase organizational performance (Hansen 1995 ; Westland and Au 1997 ). These believers suspected an online presence could offer advantages to their customers, while providing a shopping experience similar to the traditional bricks-and-mortar store (Jarvenpaa and Todd 1996 ). The advantages included providing around the clock access for customers, reducing geographic boundaries to provide access to new markets, and enabling immediate communication with customers.

The prediction of an explosion of online shopping became a marriage between information technology experts and marketing professionals. Most would believe the information technology researchers were studying the Internet technology and its advantages, while the marketers were focused on the consumer’s use of the technology. As technology advanced, more marketing activities emerged to market goods and services via the Internet. Today, Internet marketing is defined as “the use of the Internet as a virtual storefront where products are sold directly to the customer” (Kiang et al. 2000 , p. 383), or another view includes “the strategic process of creating, distributing, promoting, and pricing products for targeted customers in the virtual environment of the Internet” (Pride et al. 2007 ). This research attempts to categorize the various Internet marketing activities in a broad context including strategies such as customer relationship management (Hwang 2009 ), electronic marketplaces (Novak and Schwabe 2009 ), online auctions (Loebbecke et al. 2010 ), and electronic branding (Otim and Grover 2010 ) in tandem with unique IS issues including web site evaluation (Chiou et al. 2010 ), piracy (Smith and Telang 2009 ), security (Ransbotham and Mitra 2009 ), and technology architecture (Du et al. 2008 ).

With concepts as varied as this in one research domain, a periodic review is necessary to discover and explore new technologies such as mobile banking (Sripalawat et al. 2011 ), virtual worlds (Sutanto et al. 2011 ), and social media (de Valck et al. 2009 ) as they emerge on the Internet marketing landscape. The following sections of the paper will examine the current literature to determine what is known about the concept of Internet marketing. First, a description of the methodology for the analysis of the Internet marketing research is presented. This is followed by the results including an analysis of a smaller sample of the Internet marketing research in the top Marketing journals. Finally, the research is summarized with a discussion of the limitations of this project and suggestions for future research.

Methodology

The approach to this analysis of the Internet marketing research is to first identify trends in the Information System (IS) literature. Specifically, we wished to capture the trends pertaining to (1) the number and distribution of Internet marketing articles published in the leading journals, (2) methodologies employed in Internet marketing research, and (3) the research topics being published in this area of research. During the analysis of the literature, we attempted to identify gaps and needs in the research and therefore discuss a research agenda which allows for the progression of research (Webster and Watson 2002 ). In short, we hope to paint a representative landscape of the current Internet marketing literature base in IS in order to influence the direction of future research efforts in this important area of study.

In order to examine the current state of research on Internet marketing, the authors conducted a literature review and analysis in three phases: Phase 1 accumulated a representative pool of articles; Phase 2 classified the articles by research method; and, Phase 3 classified the research by research topic. Each of the three phases is discussed in the following paragraphs.

Phase 1: accumulation of article pool

We used the Thomson Reuters Web of Science (WoS) citation database and Google Scholar to search for research articles with a focus on Internet marketing. The search parameters were constrained based on (a) a list of top ranked journals, (b) a specific time range, and (c) key search terms.

First, the researchers chose to use the top 30 journals from Peffers and Tang’s ( 2003 ) IS journals ranking (see Table  1 ). Peffers and Tang’s ( 2003 ) ranking of ‘pure’ IS journals was adopted for this study because it was based on the responses of IS researchers who were asked to rank journals by their “relative value to the researcher and the audience as an outlet for IS research.” In Peffers and Tang’s ( 2003 ) original ranking scheme two journals, ‘Communications of the Association of Information Systems’ and ‘Information and Management,’ tied for fifth place. Peffers and Tang resolved this issue by ranking both journals in the fifth position skipping the rank of the sixth position. As noted in Table  1 , 7 of the top 30 journals were not listed in the WoS database. Consequently, all 30 journals were searched using Google Scholar and only 23 journals were searched using the WoS database. The search parameters were further constrained to a specific timeframe.

Electronic commerce and Internet marketing did not exist prior to the widespread adoption and dissemination of the public Internet and the Worldwide Web (WWW). Therefore, the search parameters were further constrained based on the historical timeframe in which technologies capable of facilitating the development of e-commerce were first introduced. The graphical user interface based browser known as Netscape Navigator was launched as a free download for public use in 1994. Many experts identify the launch of Netscape Navigator as the historical event leading to the global public’s widespread adoption and use of the Internet and the World Wide Web (WWW) (Friedman 2006 ). Therefore, the search parameters for both WoS and Google Scholar were constrained to time period of 1994 through August of 2011.

The final constraint was based on the key search term “Internet Marketing.” In both WoS and Google Scholar the search engine scanned for the term ‘Internet Marketing’ and close variations of this term found in the title, abstract, and keywords of articles published in the top 30 IS journals between January of 1994 and August of 2011 when the search was executed. There was considerable overlap in the pool of articles returned from the two search engines (WoS and Google Scholar). Once duplicate entries and non-research articles (book reviews, editorials, commentary, etc.) were removed 453 articles remained in the composite data pool. The researchers then reviewed each article and identified 42 articles that were unrelated to the topic of Internet marketing. These 42 articles represented false positives returned from the WoS and Google Scholar search engines and were subsequently removed leaving 411 articles in the final composite article data pool for analysis.

Phase 2: classification by research strategy

Once the researchers identified the articles for the final data pool, each article was examined and categorized according to its research strategy. Due to the subjective nature of research strategy classification, content analysis methods were used for the categorization process. Figure  1 illustrates steps in the content analysis process adapted from Neuendorf ( 2002 ) and successfully employed by several similar research studies (Corley et al. 2011 ; Cumbie et al. 2005 ; Jourdan et al. 2008 ). First, the research categories were adopted from Scandura and Williams ( 2000 ) (see Table  2 ), who extended the research strategies initially described by McGrath ( 1982 ). Specifically, nine categories of research strategies were selected including: Formal theory/literature reviews, sample survey, laboratory experiment, experimental simulation, field study (primary data), field study (secondary data), field experiment, judgment task, and computer simulation.

Overview of literature analysis

Second, to guard against the threats to reliability (Neuendorf 2002 ), we performed a pilot test on articles meeting the search parameters from other top journals. That is, the articles used in the pilot test (a) were not part of the data set generated in Phase 1, and (b) the data generated from the pilot test were not included in the final data analysis for this study. Researchers independently categorized the articles in the pilot test based on the best fit among the nine research strategies. After all articles in the pilot test were categorized, the researchers compared their analyses. In instances where the independent categorizations did not match the researchers re-evaluated the article collaboratively by reviewing the research strategy definitions, discussing the disagreement thoroughly, and collaboratively assigning the article to a single category. This process allowed the researchers to develop a collaborative interpretation of the research strategy definitions. Simply stated, this pilot test served as a training session for accurately categorizing the articles for this study with respect to research strategy.

Each research strategy is defined by a specific design approach and each is also associated with certain tradeoffs that researchers must make when designing a study. These tradeoffs are inherent flaws that limit the conclusions that can be drawn from a particular research strategy. These tradeoffs refer to three aspects of a study that can vary depending on the research strategy employed. These variable aspects include: generalizability from the sample to the target population (external validity); precision in measurement and control of behavioural variables (internal and construct validity); and the issue of realism of context (Scandura and Williams 2000 ).

Cook and Campbell ( 1976 ) stated that a study has generalizability when the study has external validity across times, settings, and individuals. Formal theory/literature reviews and sample surveys have a high degree of generalizability by establishing the relationship between two constructs and illustrating that this relationship has external validity. A research strategy that has low external validity but high internal validity is the laboratory experiment. In the laboratory experiment, where the degree of measurement precision is high, cause and effect relationships may be determined, but these relationships may not be generalizable for other times, settings, and populations. While the formal theory/literature reviews and sample surveys have a high degree of generalizability and the laboratory experiment has a high degree of precision of measurement, these strategies have low degree of contextual realism. The only two strategies that maximize degree of contextual realism are field studies that use either primary or secondary data because the data is collected in an organizational setting (Scandura and Williams 2000 ).

The other four strategies maximize neither generalizability, nor degree of precision in measurement, nor degree of contextual realism. This point illustrates the futility of using only one strategy when conducting Internet marketing research. Because no single strategy can maximize all types of validity, it is best for researchers to use a variety of research strategies. Table  2 contains an overview of the nine strategies and their ranking on the three strategy tradeoffs (Scandura and Williams 2000 ).

Two coders independently reviewed and classified each article according to research strategy. Only a few articles were reviewed at one sitting to minimize coder fatigue and thus protect intercoder reliability (Neuendorf 2002 ). Upon completion of the independent classification, a tabulation of agreements and disagreements were computed, intercoder crude agreement (percent of agreement) was 91.8 % percent, and intercoder reliability using Cohen’s Kappa (Cohen 1960 ) was calculated ( k  = 0.847). These two calculations were well within the acceptable ranges for intercoder crude agreement and intercoder reliability (Neuendorf 2002 ). The reliability measures were calculated prior to discussing disagreements as mandated by Weber ( 1990 ). If the original reviewers did not agree on how a particular article was coded, an additional reviewer arbitrated the discussion of how the disputed article was to be coded. This process resolved the disputes in all cases.

Phase 3: categorization by internet marketing research topic

Typically the process of categorizing research articles by a specific research topic involves an iterative cycle of brainstorming and discussion sessions among the researchers. This iterative process helps to identify common themes within the data pool of articles. Through the collaborative discussions during this process researchers can synthesize a hierarchical structure within the literature of overarching research topics and more granular level subtopics. The final outcome is a better understanding of the current state of a particular stream of research. This iterative process was modified for this specific study on the topic of Internet marketing.

During the initial stages of the current project the researchers began investigating tentative outlets for publishing a literature review on the topic of Internet marketing. A special call for papers (CFP) on the topic of Internet marketing from the journal ‘Electronic Marketing’ was identified as a potential target journal by one of the authors. Further investigation revealed that the editors had outlined six specific research topic categories for the special CFP including: Business Models of Online Marketing, The Future of Search Strategies, The Internet Advertising Landscape, Commercial Exploitation of Web 2.0 in Consumer Marketing and in an Organizational Context, Evaluation of Online Performance, and Other Topics. Each of these six research topics was accompanied by a general definition and a few examples. The researchers adopted these six research topics to categorize the articles in the data pool.

A second pilot study was performed mirroring the first pilot test as a means of training for categorizing articles by research topic. Researchers independently categorized the articles in the pilot test based on the best fit among the six research topics. After all articles in the pilot test were categorized, the researchers compared their analyses. In instances where the independent categorizations did not match, the researchers re-evaluated the article collaboratively by reviewing the research category definitions, discussing the disagreement thoroughly, and collaboratively assigning the article to a single category. This process allowed the researchers to develop a collaborative interpretation of the research topic definitions (see Table  3 ).

Once we established the category definitions, we independently placed each article in one Internet marketing category. As before, we categorized only a few articles at a time to minimize coder fatigue and thus protect intercoder reliability (Neuendorf 2002 ). Upon completion of the classification process, we tabulated agreements and disagreements, intercoder crude agreement (percent of agreement) was 86.2 %, and intercoder reliability using Cohen’s Kappa (Cohen 1960 ) for each category was calculated ( k  = .08137). Again, the latter two calculations were well within the acceptable ranges (Neuendorf 2002 ). We again calculated the reliability measures prior to discussing disagreements as mandated by Weber ( 1990 ). If the original reviewers did not agree on how a particular article was coded, a third reviewer arbitrated the discussion of how the disputed article was to be coded. This process also resolved the disputes in all cases.

In order to identify gaps and needs in the research (Webster and Watson 2002 ), we hope to paint a representative landscape of the current Internet marketing literature base in order to influence the direction of future research efforts in this important area of study. In order to examine the current state of this research, the authors conducted a literature review and analysis in three phases. Phase 1 accumulated a representative pool of Internet marketing articles, and the articles were then analyzed with respect to year of publication and journal. Phase 2 contains a short discussion of the research strategies set forth by Scandura and Williams ( 2000 ) and the results of the classification of the articles by those research strategies. Phase 3 involved the creation and use of six Internet marketing research topics, a short discussion of each topic, and the results of the classification of each article within the research topics. These results are discussed in the following paragraphs.

Results of phase 1

Using the described search criteria within the selected journals, we collected a total of 411 articles (For the complete list of articles in our sample, see Appendix A .) In phase 1, we further analyzed the articles’ year of publication and journal. Figure  2 shows the number of articles per year in our sample. Please note that 2011 only represents articles acquired using WoS and Google Scholar search engines which were available at the time (August 2011) the search was conducted. There is a general increasing trend over the 18 year period, but no articles were found to be published in 1994 & 1996. The year 2010 shows the most activity with 52 articles (12.7 %). With Internet marketing issues becoming ever more important to researchers and practitioners, this comes as no surprise. Understanding 2011 was only a partial year in our sample, we were not concerned by the difference in quantity of publications over time.

Number of Internet Marketing Articles Published Per Year

In order to identify the research strategies used by Internet marketing research articles in the top 30 Information Systems (IS) journals in our sample, Table  4 was created to show the number of Internet marketing articles in each journal broken down by research strategy. This table illustrates the high level of Internet marketing publications that use the Formal Theory/Literature Review, Sample Survey, Field Study – Primary, and Field Study – Secondary research strategies. This indicates a body of research that is still in the exploratory stages. This table also illustrates the proclivity of some journals to accept certain research strategies over others. For example, the journals Decision Support Systems , International Journal of Electronic Commerce , and Journal of Management Information Systems had articles in this data set using seven of the nine research strategies. With this information, researchers that favour certain research strategies can target their research papers to journals that favour these strategies.

Number of Internet Marketing Articles Published in Each Research Strategy Category

Results of phase 2

The results of the categorization of the 411 articles according to the nine research strategies described by Scandura and Williams ( 2000 ) are summarized in Fig.  3 and Table  5 . Of the 411 articles, 110 articles (26.8 %) were classified as Formal Theory/Literature review making it the most prevalent research strategy. This was followed by Sample Survey with 94 articles (or 22.9 %), Field Study – Secondary Data with 91 articles (22.1 %), Field Study – Primary Data with 66 articles (16.1 %), and Computer Simulation with 25 articles (6.1 %). These five research strategies composed 94 % of the articles in the sample. No articles were classified as a Judgment Task. So, the remaining three research strategies represented the remaining six percent of the sample which included Lab Experiment with 11 articles (2.7 %), Field Experiment with 11 articles (2.7 %), and Experimental Simulation with 3 articles (0.7 %).

Further analysis showing the research strategies over the 18 year period from 1994 to August 2011 (Table  6 ) illustrates that Formal Theory/Literature Review, Sample Survey, Field Study – Secondary Data, and Field Study – Primary Data are represented in almost every year of the timeframe. No articles were found in the years 1994 & 1996, and only one article was found in 1995. These four strategies are exploratory in nature and indicate the beginnings of a body of research (Scandura and Williams 2000 ). Further categorization and analysis of the articles with respect to Internet marketing topic categories was conducted in the third phase of this research project.

Results of phase 3

Table  7 shows the number of articles per Internet marketing research topic category. These six categories provided a topic area classification for all of the 411 articles in our research sample. Of the 411 articles, 41.1 % were classified as ‘Business Models of Online Marketing’ making it the most prevalent Internet marketing topic category. This category was followed by ‘The Internet Advertising Landscape’ (22.4 %), ‘Evaluation of Online Performance’ (16.5 %), and ‘Other’ (10.0 %). These four research strategies accounted for 90 % of the articles in the sample. The topic categories titled ‘Commercial Exploitation of Web 2.0 in Consumer Marketing and in an Organizational Context’ and ‘The Future of Search Strategies’ represented the remaining six per cent (5.8 %) and four percent (4.1 %) of the articles. This illustration of the share of Internet marketing research that is represented by each category reveals the amount of attention topic categories of Internet marketing research have historically received among the top 30 IS journals.

By plotting Internet marketing research topics against research strategies (Table  8 ), many of the gaps in Internet marketing research are exposed. The gaps are at the intersection of less used methodologies (Judgement Task, Experimental Simulation, Lab Experiment) and less studied domains in Internet marketing (Search Strategies and Web 2.0). We believe these gaps exist for two reasons. First, some of these research strategies are not prevalent in IS research, and some top IS journals do not accept papers that use unusual research strategies. So, researchers avoid unorthodox strategies. The reason some of these categories have not been studied is because they represent relatively new phenomena, and the research has not caught up with the business reality. The great news for researchers interested in Internet marketing is that this domain should provide research opportunities for years to come. To better illustrate the categorization process, Table  9 presents a sample of articles noting their corresponding research strategy and research topic. These articles were randomly selected as typical examples and are not meant to serve as hallmarks of a particular research strategy or research topic within Internet marketing research.

About half (49 %) of the journal articles in this study use the Formal Theory/Literature Review and Sample Survey research strategies indicating the exploratory nature of the current research. We speculate the strategies used to study these topics were prevalent for several reasons. First, these strategies are the most appropriate for the early stages of research. In these exploratory years of Internet marketing research, formal theory/literature reviews are appropriate in order to determine what other strategies are being used in the research, define the topics under investigation, and find research in reference disciplines that are conducting similar research. Second, many researchers in business schools may prefer to administer sample surveys and field studies instead of laboratory experiment, experimental simulation, judgment task, and computer simulation because of the preferences for certain research strategies in the top journals in Information Systems and Marketing. Finally, organizations are less likely to commit to certain strategies (i.e. primary & secondary field studies and field experiments) because these strategies are more expensive for the organizations. These types of research strategies are very labour intensive to the organization being studied because records will need to be examined, personnel will need to be interviewed, and senior managers will be required to devote large amounts of their expensive time to help facilitate the research project. It is interesting to note that many of the articles coded as Field Study – Secondary and Computer Simulation used historical auction and pricing data freely available from the World Wide Web to avoid this issue.

Investigating the marketing literature

In order to investigate the Internet marketing research being conducted in the top Marketing Journals, we also performed a smaller literature review using the top five ranked marketing research journals following the same methodology previously described for the top 30 ranked IS journals. This list was compiled from three recent marketing journal rankings (Hofacker et al. 2009 ; Moussa and Touzani 2010 ; and Polonsky and Whitelaw 2006 ). The data pool included 24 articles, and after screening out irrelevant articles (book reviews, opinion pieces, etc.) the remaining 22 articles were categorized by research strategy and research topic (see Appendix B ). Upon completion of the categorization process, we tabulated agreements and disagreements. Intercoder crude agreement (percent of agreement) was 95.4 % for research strategy and 90.9 % for research topic. Cohen’s Kappa could not be calculated because the sample size was too small. These two calculations were well within the acceptable ranges (Neuendorf 2002 ). The results of the literature review of the top five marketing journals are displayed in Tables  10 and 11 .

The number of articles published on the topic of Internet marketing in each of the top five ranked marketing journals is presented in Table  10 . It is interesting to note that no articles were found in Journal of Consumer Research while 16 of the 22 (72.7 %) articles in the data pool were published in Marketing Science . This could indicate (a) Marketing Science is a top outlet for Internet marketing research or (b) the other Marketing journals use keywords other than “Internet marketing” to classify this area of research. The number of articles categorized based on both research strategy and research topic is presented in Table  11 . The three research strategies with the largest number of articles among the top five marketing journals were “Formal Theory / Lit Review” (45.5 %), “Field Study - Secondary” (27.3 %), and “Field Study – Primary” (18.2 %). This indicates, like the research published in the top IS journals, the Internet marketing research published in the top marketing journals is also still in the exploratory stages.

Fourteen of the twenty-two articles (63.6 %) were categorized within the research topic labelled “the Internet Advertising Landscape” while no articles were categorized within the research topics “Commercial Exploitation of Web 2.0” or “Evaluation of Online Performance.” In contrast to the analysis of the top thirty ranked IS journals in which the top three research topics were “Business Models of Online Marketing” (41.1 %), “the Internet Advertising Landscape” (22.4 %), and Evaluation of Online Performance (16.5 %); the top three research topics within the top five marketing journals were “the Internet marketing Landscape” (63.6 %), “Business Models of Online Marketing” (13.6 %), and “Other Topics” (13.6 %). Due to the small number of articles in the sample, it is difficult to make any statements regarding trends in the Internet marketing research in the top Marketing journals.

Limitations and directions for future research

The current analysis of the Internet marketing literature is not without limitations and should be offset with future efforts. In summary, this literature review highlights the upward trend of Internet marketing research but also the limitations of both the research strategies employed and the topics investigated. The authors would suggest future literature reviews should expand article searches to full article text searches, search a broader domain of research outlets, and include other Internet marketing related search terms. Our literature analysis is meant to serve as a representative sample of articles and not a comprehensive or exhaustive analysis of the entire population of articles published on the topic of ‘Internet marketing.’ To further investigate this body of research, future research studies could explore the diversity of the Internet marketing research domain (Lee et al. 2007 ) or revisit Ngai and Wat’s ( 2002 ) electronic commerce literature review to assess the progress of that research stream. Other studies could take a more in depth look at the various business models or Internet advertising strategies associated with Internet marketing by reviewing the literature in areas such as electronic auctions, search strategies, social media, e-tailing, and various other research domains.

As Internet marketing continues to grow, future studies should consider the role of research relative to generalizability, precision of measure, and realism of context. Future research efforts should adopt more precise measures of what is occurring in this domain. Much of the research in our sample reports the new technologies and issues in Internet marketing without attempting to explain the fundamental issues of IS research. This is to be expected as this research domain appears to still be in the exploratory stages. For researchers to continue to attempt to answer the important questions in Internet marketing, future studies need to employ a wider variety of research strategies to investigate these important issues. Scandura and Williams ( 2000 ) stated that looking at research strategies employed over time by triangulation in a given subject area can provide useful insights into how theories are developing. In addition to the lack of variety in research strategy, very little triangulation has occurred during the timeframe used to conduct this literature review. This absence of coordinated theory development causes the research in Internet marketing to appear haphazard and unfocused.

However, the good news is that many of the research strategies and topics in this research are available for future research efforts. Of particular interest to researchers and practitioners would be studies observing consumer behaviour in real time using lab and field experiments or measuring purchasing behaviour from using stored click stream data in a secondary field study. We encourage researchers in fields of IS and Marketing to continue developing the body of research on this important topic using cross-disciplinary teams composed of researchers from business and the behavioural sciences. In addition, future studies could consider the six Internet marketing categories with respect to the research strategies. More specifically, each ‘zero’ appearing in Tables  8 and 11 represent gaps in the literature which provide countless opportunities for researchers to build upon the current body of published research. With this in mind, we hope this research analysis lays a foundation for developing a more complete body of knowledge relative to Internet marketing research within the fields of Information Systems and Marketing.

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J. Ken Corley II

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Responsible Editor: Christopher Patrick Holland

Appendix A – data sample (411 information systems articles)

Abbasi, A., Chen, H. C., & Nunamaker, J. F. (2008). Stylometric Identification in Electronic Markets: Scalability and Robustness. Journal of Management Information Systems, 25 (1), 49–78. doi: 10.2753/mis0742-1222250103

Adam, S. (2002). A model of Web use in direct and online marketing strategy. Electronic Markets, 12 (4), 262–269.

Albrecht, C. C., Dean, D. L., & Hansen, J. V. (2005). Marketplace and technology standards for B2B e-commerce: progress, challenges, and the state of the art. Information & Management, 42 (6), 865–875. doi: 10.1016/j.im.2004.09.003

Allen, G., & Wu, J. A. (2010). How well do shopbots represent online markets? A study of shopbots’ vendor coverage strategy. European Journal of Information Systems, 19 (3), 257–272. doi: 10.1057/ejis.2010.6

Amblee, N., & Bui, T. (2008). Can brand reputation improve the odds of being reviewed on-line? International Journal of Electronic Commerce, 12 (3), 11–28.

Amir, Y., Awerbuch, B., & Borgstrom, R. S. (2000). A cost-benefit framework for online management of a metacomputing system. Decision Support Systems, 28 (1–2), 155–164. doi: 10.1016/s0167-9236(99)00081-0

Anckar, B., & Walden, P. (2000). Destination Maui? An exploratory assessment of the efficacy of self-booking in travel. Electronic Markets, 10 (2), 110–119.

Animesh, A., Ramachandran, V., & Viswanathan, S. (2010). Quality Uncertainty and the Performance of Online Sponsored Search Markets: An Empirical Investigation. Information Systems Research, 21 (1), 190–201. doi: 10.1287/isre.1080.0222

Animesh, A., Viswanathan, S., & Agarwal, R. (2011). Competing “Creatively” in Sponsored Search Markets: The Effect of Rank, Differentiation Strategy, and Competition on Performance. Information Systems Research, 22 (1), 153–169.

Antony, S., Lin, Z. X., & Xu, B. (2006). Determinants of escrow service adoption in consumer-to-consumer online auction market: An experimental study. Decision Support Systems, 42 (3), 1889–1900. doi: 10.1016/j.dss.2006.04.012

Apigian, C. H., Ragu-Nathan, B. S., & Ragu-Nathan, T. (2006). Strategic profiles and Internet Performance: An empirical investigation into the development of a strategic Internet system. Information & Management, 43 (4), 455–468.

Aron, R., & Clemons, E. K. (2001). Achieving the optimal balance between investment in quality and investment in self-promotion for information products. Journal of Management Information Systems, 18 (2), 65–88.

Arunkundram, R., & Sundararajan, A. (1998). An economic analysis of electronic secondary markets: installed base, technology, durability and firm profitability. Decision Support Systems, 24 (1), 3–16. doi: 10.1016/s0167-9236(98)00059-1

Ayanso, A., & Yoogalingam, R. (2009). Profiling Retail Web Site Functionalities and Conversion Rates: A Cluster Analysis. International Journal of Electronic Commerce, 14 (1), 79–113. doi: 10.2753/jec1086-4415140103

Ba, S., Stallaert, J., Whinston, A. B., & Zhang, H. (2005). Choice of transaction channels: The effects of product characteristics on market evolution. Journal of Management Information Systems, 21 (4), 173–197.

Bai, X. (2011). Predicting consumer sentiments from online text. Decision Support Systems, 50 (4), 732–742. doi: 10.1016/j.dss.2010.08.024

Bakos, J. Y., & Nault, B. R. (1997). Ownership and investment in electronic networks. Information Systems Research, 8 (4), 321–341. doi: 10.1287/isre.8.4.321

Bakos, Y., & Katsamakas, E. (2008). Design and ownership of two-sided networks: Implications for Internet platforms. Journal of Management Information Systems, 25 (2), 171–202. doi: 10.2753/mis0742-1222250208

Bakos, Y., Lucas, H. C., Oh, W., Simon, G., Viswanathan, S., & Weber, B. W. (2005). The impact of e-commerce on competition in the retail brokerage industry. Information Systems Research, 16 (4), 352–371. doi: 10.1287/isre.1050.0064

Bampo, M., Ewing, M. T., Mather, D. R., Stewart, D., & Wallace, M. (2008). The effects of the social structure of digital networks on viral marketing performance. Information Systems Research, 19 (3), 273–290.

Bapna, R., Chang, S. A., Goes, P., & Gupta, A. (2009). Overlapping online auctions: empirical characterization of bidder strategies and auction prices. MIS Quarterly, 33 (4), 763–783.

Bapna, R., Goes, P., & Gupta, A. (2003). Replicating online Yankee auctions to analyze auctioneers’ and bidders’ strategies. Information Systems Research, 14 (3), 244–268. doi: 10.1287/isre.14.3.244.16562

Bapna, R., Jank, W., & Shmueli, G. (2008). Price formation and its dynamics in online auctions. Decision Support Systems, 44 (3), 641–656. doi: 10.1016/j.dss.2007.09.004

Barrot, C., Albers, S., Skiera, B., & Schafers, B. (2010). Vickrey vs. eBay: Why Second-Price Sealed-Bid Auctions Lead to More Realistic Price-Demand Functions. International Journal of Electronic Commerce, 14 (4), 7–38. doi: 10.2753/jec1086-4415140401

Basu, A., & Muylle, S. (2003). Online support for commerce processes by web retailers* 1. Decision Support Systems, 34 (4), 379–395.

Beech, J., Chadwick, S., & Tapp, A. (2000). Scoring with the Net-the Cybermarketing of English Football Clubs. Electronic Markets, 10 (3), 176–184.

Belanger, F., Hiller, J. S., & Smith, W. J. (2002). Trustworthiness in electronic commerce: the role of privacy, security, and site attributes. The Journal of Strategic Information Systems, 11 (3–4), 245–270.

Bell, D., de Cesare, S., Iacovelli, N., Lycett, M., & Merico, A. (2007). A framework for deriving semantic web services. Information Systems Frontiers, 9 (1), 69–84. doi: 10.1007/s10796-006-9018-z

Benbunan-Fich, R., & Fich, E. M. (2004). Effects of Web traffic announcements on firm value. International Journal of Electronic Commerce, 8 (4), 161–181.

Bergen, M. E., Kauffman, R. J., & Lee, D. (2005). Beyond the hype of frictionless markets: Evidence of heterogeneity in price rigidity on the Internet. Journal of Management Information Systems, 22 (2), 57–89.

Bhargava, H. K., & Choudhary, V. (2004). Economics of an information intermediary with aggregation benefits. Information Systems Research, 15 (1), 22–36. doi: 10.1287/isre.1040.0014

Bhatnagar, A., & Papatla, P. (2001). Identifying locations for targeted advertising on the Internet. International Journal of Electronic Commerce, 5 (3), 23–44.

Bhattacharjee, S., Gopal, R., Lertwachara, K., & Marsden, J. R. (2006). Whatever happened to payola? An empirical analysis of online music sharing. Decision Support Systems, 42 (1), 104–120.

Blount, Y. (2011). Employee management and service provision: a conceptual framework. Information Technology & People, 24 (2), 134–157. doi: 10.1108/09593841111137331

Bock, G. W., Lee, S. Y. T., & Li, H. Y. (2007). Price comparison and price dispersion: products and retailers at different Internet maturity stages. International Journal of Electronic Commerce, 11 (4), 101–124.

Bockstedt, J. C., Kauffman, R. J., & Riggins, F. J. (2006). The move to artist-led on-line music distribution: A theory-based assessment and prospects for structural changes in the digital music market. International Journal of Electronic Commerce, 10 (3), 7–38. doi: 10.2753/jec1086-4415100301

Bolton, G., Loebbecke, C., & Ockenfels, A. (2008). Does competition promote trust and trustworthiness in online trading? An experimental study. Journal of Management Information Systems, 25 (2), 145–169. doi: 10.2753/mis0742-1222250207

Browne, G. J., Durrett, J. R., & Wetherbe, J. C. (2004). Consumer reactions toward clicks and bricks: investigating buying behaviour on-line and at stores. Behaviour & Information Technology, 23 (4), 237–245. doi: 10.1080/01449290410001685411

Bunduchi, R. (2005). Business relationships in Internet-based electronic markets: the role of goodwill trust and transaction costs. Information Systems Journal, 15 (4), 321–341. doi: 10.1111/j.1365-2575.2005.00199.x

Burgess, S., Sellitto, C., Cox, C., & Buultjens, J. (2009). Trust perceptions of online travel information by different content creators: Some social and legal implications. Information Systems Frontiers , 1–15.

Byers, R. E., & Lederer, P. J. (2001). Retail bank services strategy: A model of traditional, electronic, and mixed distribution choices. Journal of Management Information Systems, 18 (2), 133–156.

Cao, Q., Duan, W., & Gan, Q. (2010). Exploring Determinants of Voting for the. Decision Support Systems .

Cao, Y., Gruca, T. S., & Klemz, B. R. (2003). Internet pricing, price satisfaction, and customer satisfaction. International Journal of Electronic Commerce, 8 (2), 31–50.

Castañeda, J. A., Muñoz-Leiva, F., & Luque, T. (2007). Web Acceptance Model (WAM): Moderating effects of user experience. Information & Management, 44 (4), 384–396.

Cazier, J. A., Shao, B. B. M., & Louis, R. D. S. (2007). Sharing information and building trust through value congruence. Information Systems Frontiers, 9 (5), 515–529.

Chang, H. H., & Chen, S. W. (2009). Consumer perception of interface quality, security, and loyalty in electronic commerce. Information & Management, 46 (7), 411–417.

Chang, M. K., Cheung, W. M., & Lai, V. S. (2005). Literature derived reference models for the adoption of online shopping. Information & Management, 42 (4), 543–559. doi: 10.1016/s0378-7206(04)00051-5

Changa, K. C., Jackson, J., & Grover, V. (2003). E-commerce and corporate strategy: an executive perspective. Information & Management, 40 (7), 663–675. doi: 10.1016/s0378-7206(02)00095-2

Chellappa, R. K., & Kumar, K. R. (2005). Examining the role of “Free” product-augmenting Online services in pricing and customer retention strategies. Journal of Management Information Systems, 22 (1), 355–377.

Chellappa, R. K., & Shivendu, S. (2003). Economic implications of variable technology standards for movie piracy in a global context. Journal of Management Information Systems, 20 (2), 137–168.

Chellappa, R. K., Sin, R. G., & Siddarth, S. (2011). Price Formats as a Source of Price Dispersion: A Study of Online and Offline Prices in the Domestic US Airline Markets. Information Systems Research, 22 (1), 83–98. doi: 10.1287/isre.1090.0264

Chen, C. C., Wu, C. S., & Wu, R. C. F. (2006). e-Service enhancement priority matrix: The case of an IC foundry company. Information & Management, 43 (5), 572–586. doi: 10.1016/j.im.2006.01.002

Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information & Management, 39 (8), 705–719. doi: 10.1016/s0378-7206(01)00127-6

Chen, P. Y., & Hitt, L. M. (2002). Measuring switching costs and the determinants of customer retention in Internet-enabled businesses: A study of the Online brokerage industry. Information Systems Research, 13 (3), 255–274. doi: 10.1287/isre.13.3.255.78

Cheng, F. F., & Wu, C. S. (2010). Debiasing the framing effect: The effect of warning and involvement. Decision Support Systems, 49 (3), 328–334.

Cheng, H. K., & Dogan, K. (2008). Customer-centric marketing with Internet coupons. Decision Support Systems, 44 (3), 606–620. doi: 10.1016/j.dss.2007.09.001

Cheng, T. C. E., Lam, D. Y. C., & Yeung, A. C. L. (2006). Adoption of Internet banking: An empirical study in Hong Kong. Decision Support Systems, 42 (3), 1558–1572. doi: 10.1016/j.dss.2006.01.002

Cheng, Z., & Nault, B. R. (2007). Internet channel entry: retail coverage and entry cost advantage. Information Technology & Management, 8 (2), 111–132. doi: 10.1007/s10799-007-0015-9

Cheung, K. W., Kwok, J. T., Law, M. H., & Tsui, K. C. (2003). Mining customer product rating for personalized marketing. Decision Support Systems, 35 (2), 231–243. doi: 10.1016/s0167-9236(02)00108-2

Chiou, W. C., Lin, C. C., & Perng, C. (2010). A strategic framework for website evaluation based on a review of the literature from 1995–2006. Information & Management, 47 (5–6), 282–290.

Chircu, A. M., & Kauffman, R. J. (2000a). Limits to value in electronic commerce-related IT investments. Journal of Management Information Systems, 17 (2), 59–80.

Chircu, A. M., & Kauffman, R. J. (2000b). Reintermediation strategies in business-to-business electronic commerce. International Journal of Electronic Commerce, 4 (4), 7–42.

Chircu, A. M., & Mahajan, V. (2006). Managing electronic commerce retail transaction costs for customer value. Decision Support Systems, 42 (2), 898–914. doi: 10.1016/j.dss.2005.07.011

Cho, V. (2006a). Factors in the adoption of third-party B2B portals in the textile industry. Journal of Computer Information Systems, 46 (3), 18–31.

Cho, V. (2006b). A study of the roles of trusts and risks in information-oriented online legal services using an integrated model. Information & Management, 43 (4), 502–520. doi: 10.1016/j.im.2005.12.002

Choi, J., Lee, S. M., & Soriano, D. R. (2009). An empirical study of user acceptance of fee-based online content. Journal of Computer Information Systems, 49 (3), 60–70.

Choudhary, V. (2010). Use of pricing schemes for differentiating information goods. Information Systems Research, 21 (1), 78.

Choudhury, V., & Karahanna, E. (2008). The relative advantage of electronic channels: A multidimensional view. MIS Quarterly, 32 (1), 179–200.

Christiaanse, E., Van Diepen, T., & Damsgaard, J. (2004). Proprietary versus Internet technologies and the adoption and impact of electronic marketplaces. Journal of Strategic Information Systems, 13 (2), 151–165. doi: 10.1016/j.jsis.2004.02.004

Chua, C. E. H., & Wareham, J. (2008). Parasitism and Internet auction fraud: An exploration. Information and Organization, 18 (4), 303–333. doi: 10.1016/j.infoandorg.2008.01.001

Chua, C. E. H., Wareham, J., & Robey, D. (2007). The role of online trading communities in managing Internet auction fraud. MIS Quarterly, 31 (4), 759–781.

Chun, S. H., & Kim, J. C. (2005). Pricing strategies in B2C electronic commerce: analytical and empirical approaches. Decision Support Systems, 40 (2), 375–388. doi: 10.1016/j.dss.2004.04.012

Clemons, E. K. (2009a). Business models for monetizing Internet applications and Web sites: Experience, theory, and predictions. Journal of Management Information Systems, 26 (2), 15–41.

Clemons, E. K. (2009b). The complex problem of monetizing virtual electronic social networks. Decision Support Systems, 48 (1), 46–56.

Crowston, K., & Myers, M. D. (2004). Information technology and the transformation of industries: three research perspectives. Journal of Strategic Information Systems, 13 (1), 5–28. doi: 10.1016/j.jsis.2004.02.001

Currie, W. L., & Parikh, M. A. (2006). Value creation in web services: An integrative model. Journal of Strategic Information Systems, 15 (2), 153–174. doi: 10.1016/j.jsis.2005.10.001

Cyr, D., Bonanni, C., Bowes, J., & Ilsever, J. (2005). Beyond trust: Web site design preferences across cultures. Journal of Global Information Management, 13 (4), 25.

Dai, Q. Z., & Kauffman, R. J. (2002). Business models for Internet-based B2B electronic markets. International Journal of Electronic Commerce, 6 (4), 41–72.

Datta, P. (2011). A preliminary study of ecommerce adoption in developing countries. Information Systems Journal, 21 (1), 3–32. doi: 10.1111/j.1365-2575.2009.00344.x

Datta, P., & Chatterjee, S. (2008). The economics and psychology of consumer trust in intermediaries in electronic markets: the EM-Trust Framework. European Journal of Information Systems, 17 (1), 12–28. doi: 10.1057/palgrave.ejis.3000729

Davis, A., & Khazanchi, D. (2008). An empirical study of online word of mouth as a predictor for multi product category e-Commerce Sales. Electronic Markets, 18 (2).

de Valck, K., van Bruggen, G. H., & Wierenga, B. (2009). Virtual communities: A marketing perspective. Decision Support Systems, 47 (3), 185–203. doi: 10.1016/j.dss.2009.02.008

De Wulf, K., Schillewaert, N., Muylle, S., & Rangarajan, D. (2006). The role of pleasure in web site success. Information & Management, 43 (4), 434–446.

Dehning, B., Richardson, V. J., Urbaczewski, A., & Wells, J. D. (2004). Reexamining the value relevance of e-commerce initiatives. Journal of Management Information Systems, 21 (1), 55–82.

Dellaert, B. G. C., & Dabholkar, P. A. (2009). Increasing the attractiveness of mass customization: The role of complementary on-line services and range of options. International Journal of Electronic Commerce, 13 (3), 43–70.

Dellarocas, C., Gao, G. D., & Narayan, R. (2010). Are consumers more likely to contribute online reviews for hit or niche products? Journal of Management Information Systems, 27 (2), 127–157. doi: 10.2753/mis0742-1222270204

Devaraj, S., Fan, M., & Kohli, R. (2006). Examination of online channel preference: Using the structure-conduct-outcome framework. Decision Support Systems, 42 (2), 1089–1103. doi: 10.1016/j.dss.2005.09.004

Dewan, R., Jing, B., & Seidmann, A. (2000). Adoption of Internet-based product customization and pricing strategies. Journal of Management Information Systems, 17 (2), 9–28.

Dewan, R. M., & Freimer, M. L. (2003). Consumers prefer bundled add-ins. Journal of Management Information Systems, 20 (2), 99–111.

Dewan, R. M., Freimer, M. L., Seidmann, A., & Zhang, J. (2004). Web portals: Evidence and analysis of media concentration. Journal of Management Information Systems, 21 (2), 181–199.

Dewan, S., & Ren, F. (2007). Risk and return of information technology initiatives: Evidence from electronic commerce announcements. Information Systems Research, 18 (4), 370–394. doi: 10.1287/isre.1070.0120

Dhar, V., & Ghose, A. (2010). Sponsored Search and Market Efficiency. Information Systems Research, 21 (4), 760–772. doi: 10.1287/isre.1100.0315

Dos Santos, B. L., & Peffers, K. (1998). Competitor and vendor influence on the adoption of innovative applications in electronic commerce. Information & Management, 34 (3), 175–184. doi: 10.1016/s0378-7206(98)00053-6

Dou, W. Y., Lim, K. H., Su, C. T., Zhou, N., & Cui, N. (2010). Brand positioning strategy using search engine marketing. MIS Quarterly, 34 (2), 261–279.

Du, A. Y., Geng, X. J., Gopal, R. D., Ramesh, R., & Whinston, A. B. (2008). Topographically discounted Internet infrastructure resources: a panel study and econometric analysis. Information Technology & Management, 9 (2), 135–146. doi: 10.1007/s10799-007-0034-6

Du, T. C., Li, E. Y., & Wei, E. (2005). Mobile agents for a brokering service in the electronic marketplace. Decision Support Systems, 39 (3), 371–383.

Duan, W., Gu, B., & Whinston, A. B. (2009). Informational cascades and software adoption on the internet: an empirical investigation. MIS Quarterly, 33 (1), 23–48.

Duan, W. J. (2010). Analyzing the impact of intermediaries in electronic markets: an empirical investigation of online consumer-to-consumer (C2C) auctions. Electronic Markets, 20 (2), 85–93. doi: 10.1007/s12525-010-0034-y

Dutta, A. (2001). Business planning for network services: A systems thinking approach. Information Systems Research, 12 (3), 260–285. doi: 10.1287/isre.12.3.260.9713

Dwivedi, Y. K., Papazafeiropoulou, A., Brinkman, W. P., & Lal, B. (2010). Examining the influence of service quality and secondary influence on the behavioural intention to change Internet service provider. Information Systems Frontiers, 12 (2), 207–217. doi: 10.1007/s10796-008-9074-7

Easley, R. F., Wood, C. A., & Barkataki, S. (2010). Bidding Patterns, Experience, and Avoiding the Winner’s Curse in Online Auctions. Journal of Management Information Systems, 27 (3), 241–268. doi: 10.2753/mis0742-1222270309

Edelman, B., & Ostrovsky, M. (2007). Strategic bidder behavior in sponsored search auctions. Decision Support Systems, 43 (1), 192–198. doi: 10.1016/j.dss.2006.08.008

El Sawy, O. A., Malhotra, A., Gosain, S., & Young, K. M. (1999). IT-intensive value innovation in the electronic economy: Insights from Marshall Industries. MIS Quarterly, 23 (3), 305–335.

Erat, P., Desouza, K. C., Schafer-Jugel, A., & Kurzawa, M. (2006). Business customer communities and knowledge sharing: exploratory study of critical issues. European Journal of Information Systems, 15 (5), 511–524. doi: 10.1057/palgrave.ejis.3000643

Even, A., Shankaranarayanan, G., & Berger, P. D. (2010). Evaluating a model for cost-effective data quality management in a real-world CRM setting. Decision Support Systems .

Flavián, C., Guinalíu, M., & Gurrea, R. (2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information & Management, 43 (1), 1–14.

Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19 (3), 291–313. doi: 10.1287/isre.1080.0193

Gallaugher, J. M., Auger, P., & BarNir, A. (2001). Revenue streams and digital content providers: an empirical investigation. Information & Management, 38 (7), 473–485. doi: 10.1016/s0378-7206(00)00083-5

Gao, S. J., Wang, H. Q., Xu, D. M., & Wang, Y. F. (2007). An intelligent agent-assisted decision support system for family financial planning. Decision Support Systems, 44 (1), 60–78. doi: 10.1016/j.dss.2007.03.001

Garcia, R., & Gil, R. (2008). A web ontology for copyright contract management. International Journal of Electronic Commerce, 12 (4), 99–113. doi: 10.2753/jec1086-4415120404

Gauzente, C. (2009). Information search and paid results—proposition and test of a hierarchy-of-effect model. Electronic Markets, 19 (2), 163–177.

Gefen, D., Rose, G. M., Warkentin, M., & Pavlou, P. A. (2005). Cultural diversity and trust in IT adoption: A comparison of potential e-voters in the USA and South Africa. Journal of Global Information Management, 13 (1), 54–78. doi: 10.4018/jgim.2005010103

Ghose, A. (2009). Internet exchanges for used goods: an empirical analysis of trade patterns and adverse selection. MIS Quarterly, 33 (2), 263–291.

Ghose, A., Mukhopadhyay, T., & Rajan, U. (2007). The impact of Internet referral services on a supply chain. Information Systems Research, 18 (3), 300–319. doi: 10.1287/isre.1070.0130

Ghose, A., Smith, M. D., & Telang, R. (2006). Internet exchanges for used books: An empirical analysis of product cannibalization and welfare impact. Information Systems Research, 17 (1), 3–19. doi: 10.1287/isre.1050.0072

Ghose, A., & Yao, Y. L. (2011). Using Transaction Prices to Re-Examine Price Dispersion in Electronic Markets. Information Systems Research, 22 (2), 269–288. doi: 10.1287/isre.1090.0252

Glover, S., & Benbasat, I. (2010). A Comprehensive Model of Perceived Risk of E-Commerce Transactions. International Journal of Electronic Commerce, 15 (2), 47–78.

Gopal, R. D., Ramesh, R., & Whinston, A. B. (2003). Microproducts in a digital economy: Trading small, gaining large. International Journal of Electronic Commerce, 8 (2), 9–29.

Gopal, R. D., Tripathi, A. K., & Walter, Z. D. (2006). Economics of first-contact email advertising. Decision Support Systems, 42 (3), 1366–1382.

Gorman, M. F., Salisbury, W. D., & Brannon, I. (2009). Who wins when price information is more ubiquitous? An experiment to assess how infomediaries influence price. Electronic Markets, 19 (2–3), 151–162. doi: 10.1007/s12525-009-0009-z

Granados, N., Gupta, A., & Kauffman, R. J. (2008). Designing online selling mechanisms: Transparency levels and prices. Decision Support Systems, 45 (4), 729–745. doi: 10.1016/j.dss.2007.12.005

Granados, N., Gupta, A., & Kauffman, R. J. (2010). Information Transparency in Business-to-Consumer Markets: Concepts, Framework, and Research Agenda. Information Systems Research, 21 (2), 207–226. doi: 10.1287/isre.1090.0249

Granados, N. F., Gupta, A., & Kauffman, R. J. (2006). The impact of IT on market information and transparency: A unified theoretical framework. Journal of the Association for Information Systems, 7 (3), 148–178.

Granados, N. F., Kauffman, R. J., & King, B. (2008). How has electronic travel distribution been transformed? A test of the theory of newly vulnerable markets. Journal of Management Information Systems, 25 (2), 73–95. doi: 10.2753/mis0742-1222250204

Gregg, D. G., & Scott, J. E. (2006). The role of reputation systems in reducing on-line auction fraud. International Journal of Electronic Commerce, 10 (3), 95–120. doi: 10.2753/jec1086-4415100304

Gregor, S., & Jones, K. (1999). Beef producers online: Diffusion theory applied. Information Technology & People, 12 (1), 71–85.

Grenci, I. T. (2004). An adaptable customer decision support system for custom configurations. Journal of Computer Information Systems, 45 (2), 56–62.

Grover, V., & Saeed, K. A. (2004). Strategic orientation and performance of Internet-based businesses. Information Systems Journal, 14 (1), 23–42. doi: 10.1111/j.1365-2575.2004.00161.x

Gundepudi, P., Rudi, N., & Seidmann, A. (2001). Forward versus spot buying of information goods. Journal of Management Information Systems, 18 (2), 107–131.

Gupta, A., Su, B., & Walter, Z. (2004). Risk profile and consumer shopping behavior in electronic and traditional channels. Decision Support Systems, 38 (3), 347–367.

Gupta, A., Su, B. C., & Walter, Z. (2004). An empirical study of consumer switching from traditional to electronic channels: A purchase-decision process perspective. International Journal of Electronic Commerce, 8 (3), 131–161.

Gupta, S., & Kim, H. W. (2007). The moderating effect of transaction experience on the decision calculus in on-line repurchase. International Journal of Electronic Commerce, 12 (1), 127–158.

Hansen, H. R. (1995). Conceptual-framework and guidelines for the implementation of mass information-systems. Information & Management, 28 (2), 125–142. doi: 10.1016/0378-7206(95)94021-4

Harrison McKnight, D., Choudhury, V., & Kacmar, C. (2002). The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The Journal of Strategic Information Systems, 11 (3–4), 297–323.

Harrison, T., & Waite, K. (2006). A time-based assessment of the influences, uses and benefits of intermediary website adoption. Information & Management, 43 (8), 1002–1013.

Hassanein, K., & Head, M. (2005). The impact of infusing social presence in the web interface: An investigation across product types. International Journal of Electronic Commerce, 10 (2), 31–55.

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Corley, J.K., Jourdan, Z. & Ingram, W.R. Internet marketing: a content analysis of the research. Electron Markets 23 , 177–204 (2013). https://doi.org/10.1007/s12525-012-0118-y

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Received : 08 November 2011

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Published : 31 January 2013

Issue Date : September 2013

DOI : https://doi.org/10.1007/s12525-012-0118-y

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