research paper on electronic marketing

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.

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.

Article   Google Scholar  

Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20 (1), 37–46.

Corley, J. K., Jourdan, S. Z., & Rainer, R. K. (2011). Privacy research: application of content analysis to assess a contemporary area of research. International Journal of Electronic Marketing and Retailing, 4 (2/3), 129–150.

Google Scholar  

Cook, T. D., & Campbell, D. T. (1976). The design and conduct of quasi-experiments and true experiments in field settings. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology . Chicago: Rand McNally.

Cumbie, B. A., Jourdan, S. Z., Peachey, T. A., Dugo, T. M., & Craighead, C. W. (2005). Enterprise resource planning research: where are we now and where should we go from here? Journal of Information Technology Theory & Application, 7 (2), 21–36.

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 .

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 and Management, 9 (2), 135–146. doi: 10.1007/s10799-007-0034-6 .

Friedman, T. L. (2006). The world is flat, release 2.0 . New York: Fartar Straus, and Giroux.

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 .

Hofacker, C. F., Gleim, M. R., & Lawson, S. J. (2009). Revealed reader preference for marketing journals. Journal of the Academy of Marketing Science, 37 (2), 238–247.

Hwang, Y. (2009). The impact of uncertainty avoidance, social norms and innovativeness on trust and ease of use in electronic customer relationship management. Electronic Markets, 19 (2–3), 89–98. doi: 10.1007/s12525-009-0007-1 .

Jarvenpaa, S. L., & Todd, P. A. (1996). Consumer reactions to electronic shopping on the World Wide Web. International Journal of Electronic Commerce, 1 (2), 59–88.

Jourdan, Z., Rainer, R. K., Jr., & Marshall, T. (2008). Business intelligence: a framework for research categorization. Information Systems Management, 25 (2), 121–131.

Kiang, M. Y., Raghu, T. S., & Shang, K. H. M. (2000). Marketing on the internet - who can benefit from an online marketing approach? Decision Support Systems, 27 (4), 383–393.

Lee, S. M., Hwang, T., & Kim, J. (2007). An analysis of diversity in electronic commerce research. International Journal of Electronic Commerce, 12 (1), 31–67.

Loebbecke, C., Powell, P., & Weiss, T. (2010). Repeated use of online auctions: investigating individual seller motivations. Electronic Markets, 20 (2), 105–117.

McGrath, J. (1982). Dilemmatics: the study of research choices and dilemmas. In J. E. McGrath, J. Martin, & R. A. Kilka (Eds.), Judgment calls in research . Beverly Hills: SAGE Publications.

Moussa, S., & Touzani, M. (2010). Ranking marketing journals using the Google scholar-based hg-index. Journal of Informetrics, 4 (1), 107–117.

Neuendorf, K. A. (2002). The content analysis guidebook . Thousand Oaks: SAGE Publications.

Ngai, E., & Wat, F. (2002). A literature review and classification of electronic commerce research. Information Management, 39 (5), 415–429.

Novak, J., & Schwabe, G. (2009). Designing for reintermediation in the brick-and-mortar world: towards the travel agency of the future. Electronic Markets, 19 (1), 15–29. doi: 10.1007/s12525-009-0003-5 .

Otim, S., & Grover, V. (2010). E-commerce: a brand name’s curse. Electronic Markets, 20 (2), 147–160. doi: 10.1007/s12525-010-0039-6 .

Peffers, K., & Tang, Y. (2003). Identifying and evaluating the universe of outlets for information systems research: ranking the journals. Journal of Information Technology Theory and Application, 5(1), 63–84.

Pride, W. M., & Ferrell, O. C. (2007). Foundations of Marketing (2nd ed.). Boston, MA: Houghton Mifflin.

Polonsky, M. J., & Whitelaw, P. (2006). Amulti-dimensional examination of marketing journals by North American academics. Marketing Education Review, 16 (3), 59–72.

Ransbotham, S., & Mitra, S. (2009). Choice and chance: a conceptual model of paths to information security compromise. Information Systems Research, 20 (1), 121–139.

Scandura, T. A., & Williams, E. A. (2000). Research methodology in management: current practices, trends, and implications for future research. Academy of Management Journal, 43 (6), 1248–1264.

Smith, M. D., & Telang, R. (2009). Competing with free: the impact of movie broadcasts on DVD sales and internet piracy. MIS Quarterly, 33 (2), 321–338.

Sripalawat, J., Thongmak, M., & Ngramyarn, A. (2011). M-banking in metropolitan Bangkok and a comparison with other countries. The Journal of Computer Information Systems, 51 (3), 67–76.

Sutanto, J., Phang, C. W., Tan, C. H., & Lu, X. (2011). Dr. Jekyll vis-a-vis Mr. Hyde: Personality variation between virtual and real worlds. Information & Management, 48 (1), 19–26.

Weber, R. H. (1990). Basic content analysis (2nd ed.). Thousand Oaks: Sage Publications.

Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: writing a literature review. MIS Quarterly, 26 (2), xiii–xxiii.

Westland, J. C., & Au, G. (1997). A comparison of shopping experiences across three competing digital retailing interfaces. International Journal of Electronic Commerce, 2 (2), 57–69.

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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.

Hayne, S. C., Bugbee, B., & Wang, H. N. (2010). Bidder behaviours on eBay: collectibles and commodities. Electronic Markets, 20 (2), 95–104. doi: 10.1007/s12525-010-0036-9

Hempel, P. S., & Kwong, Y. K. (2001). B2B e-Commerce in emerging economies: i-metal.com’s non-ferrous metals exchange in China. Journal of Strategic Information Systems, 10 (4), 335–355. doi: 10.1016/s0963-8687(01)00058-0

Hennig-Thurau, T., & Walsh, G. (2003). Electronic word-of-mouth: Motives for and consequences of reading customer articulations on the Internet. International Journal of Electronic Commerce, 8 (2), 51–74.

Hinz, O., Hann, I. H., & Spann, M. (2011). Price discrimination in e-commerce? an examination of dynamic pricing in name-your-own price markets. MIS Quarterly, 35 (1), 81–98.

Hinz, O., & Spann, M. (2008). The impact of information diffusion on bidding behavior in secret reserve price auctions. Information Systems Research, 19 (3), 351–368.

Hinz, O., & Spann, M. (2010). Managing information diffusion in Name-Your-Own-Price auctions. Decision Support Systems, 49 (4), 474–485.

Ho, K. K. W., Yoo, B., Yu, S., & Tam, K. Y. (2007). The effect of culture and product categories on the level of use of buy-it-now (BIN) auctions by sellers. Journal of Global Information Management, 15 (4), 1–19. doi: 10.4018/jgim.2007100101

Holsapple, C. W., & Wu, J. (2008). Building effective online game websites with knowledge-based trust. Information Systems Frontiers, 10 (1), 47–60.

Hong, S. Y., & Kim, J. (2004). Architectural criteria for website evaluation - conceptual framework and empirical validation. Behaviour & Information Technology, 23 (5), 337–357. doi: 10.1080/01449290410001712753

Hou, H. P., Hu, M. Y., Chen, L., & Choi, J. Y. (2011). An enhanced model framework of personalized material flow services. Information Technology & Management, 12 (2), 149–159. doi: 10.1007/s10799-011-0099-0

Hou, J. W., & Blodgett, J. (2010). Market structure and quality uncertainty: a theoretical framework for online auction research. Electronic Markets, 20 (1), 21–32. doi: 10.1007/s12525-010-0026-y

Hsu, C. L., & Lin, J. C. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45 (1), 65–74.

Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41 (7), 853–868. doi: 10.1016/j.im.2003.08.014

Hu, N., Bose, I., Gao, Y., & Liu, L. (2010). Manipulation in digital word-of-mouth: A reality check for book reviews. Decision Support Systems .

Hu, X., Wu, G., Wu, Y., & Zhang, H. (2010). The effects of Web assurance seals on consumers’ initial trust in an online vendor: A functional perspective. Decision Support Systems, 48 (2), 407–418.

Hu, X. R., Lin, Z. X., Whinston, A. B., & Zhang, H. (2004). Hope or hype: On the viability of escrow services as trusted third parties in online auction environments. Information Systems Research, 15 (3), 236–249. doi: 10.1287/isre.1040.0027

Huang, J. H., Jiang, X. M., & Tang, Q. A. (2009). An e-commerce performance assessment model: Its development and an initial test on e-commerce applications in the retail sector of China. Information & Management, 46 (2), 100–108. doi: 10.1016/j.im.2008.12.003

Huang, M. H. (2003). Modeling virtual exploratory and shopping dynamics: an environmental psychology approach. Information & Management, 41 (1), 39–47.

Huang, M. H. (2005). Web performance scale. Information & Management, 42 (6), 841–852.

Hui, K. L., & Tam, K. Y. (2002). Software functionality: A game theoretic analysis. Journal of Management Information Systems, 19 (1), 151–184.

Hui, W., Yoo, B., & Tam, K. Y. (2008). Economics of shareware: How do uncertainty and piracy affect shareware quality and brand premium? Decision Support Systems, 44 (3), 580–594. doi: 10.1016/j.dss.2007.07.009

Huizingh, E. (2000). The content and design of web sites: an empirical study. Information & Management, 37 (3), 123–134.

Hwang, Y. (2009). The impact of uncertainty avoidance, social norms and innovativeness on trust and ease of use in electronic customer relationship management. Electronic Markets, 19 (2–3), 89–98. doi: 10.1007/s12525-009-0007-1

Jih, W. J. K., & Lee, S. F. (2003). An exploratory analysis of relationships between cellular phone uses shopping motivators and lifestyle indicators. Journal of Computer Information Systems, 44 (2), 65–73.

Joh, Y. H., & Lee, J. K. (2003). Buyer’s customized directory management over sellers’ e-catalogs: logic programming approach. Decision Support Systems, 34 (2), 197–212.

Joo, J. (2007). An empirical study on the relationship between customer value and repurchase intention in Korean Internet shopping malls. Journal of Computer Information Systems, 48 (1), 53–62.

Josang, A., Ismail, R., & Boyd, C. (2007). A survey of trust and reputation systems for online service provision. Decision Support Systems, 43 (2), 618–644. doi: 10.1016/j.dss.2005.05.019

Jukic, N., Jukic, B., Meamber, L., & Nezlek, G. (2002). Employing a multilevel secure approach in CRM systems. Journal of Information Technology Theory and Application, 4 (2), 4.

Junglas, I. A., Johnson, N. A., & Spitzmuller, C. (2008). Personality traits and concern for privacy: an empirical study in the context of location-based services. European Journal of Information Systems, 17 (4), 387–402. doi: 10.1057/ejis.2008.29

Juul, N. C., & Jorgensen, N. (2003). The security hole in WAP: An analysis of the network and business rationales underlying a failure. International Journal of Electronic Commerce, 7 (4), 73–92.

Kagie, M., van Wezel, M., & Groenen, P. J. F. (2008). A graphical shopping interface based on product attributes. Decision Support Systems, 46 (1), 265–276.

Kalanidhi, S. (2001). Value creation in a network: The role of pricing and revenue optimization and enterprise profit optimization (TM). Information Systems Frontiers, 3 (4), 465–470. doi: 10.1023/a:1012828921804

Kamssu, A. J., Reithel, B. J., & Ziegelmayer, J. L. (2003). Information technology and financial performance: The impact of being an Internet-dependent firm on stock returns. Information Systems Frontiers, 5 (3), 279–288. doi: 10.1023/a:1025649311259

Kannan, P., & Kopalle, P. K. (2001). Dynamic pricing on the Internet: Importance and implications for consumer behavior. International Journal of Electronic Commerce, 5 (3), 63–83.

Karageorgos, A., Thompson, S., & Mehandjiev, N. (2002). Agent-based system design for B2B electronic commerce. International Journal of Electronic Commerce, 7 (1), 59–90.

Karuga, G. G., Khraban, A. M., Nair, S. K., & Rice, D. O. (2001). AdPalette: an algorithm for customizing online advertisements on the fly. Decision Support Systems, 32 (2), 85–106.

Kassim, N. M., & Abdullah, N. A. (2008). Customer loyalty in e-Commerce settings: An empirical study. Electronic Markets, 18 (3), 275–290.

Kauffman, R. J., & Lee, D. (2010). A multi-Level theory approach to understanding price rigidity in Internet retailing. Journal of the Association for Information Systems, 11 (6), 303–338.

Kauffman, R. J., Lee, D., Lee, J., & Yoo, B. (2009). A hybrid firm’s pricing strategy in Electronic Commerce under channel migration. International Journal of Electronic Commerce, 14 (1), 11–54. doi: 10.2753/jec1086-4415140101

Kauffman, R. J., & Walden, E. A. (2001). Economics and electronic commerce: survey and research directions. International Journal of Electronic Commerce, 5 (4), 5–117.

Kauffman, R. J., & Wang, B. (2001). New buyers’ arrival under dynamic pricing market microstructure: The case of group-buying discounts on the Internet. Journal of Management Information Systems, 18 (2), 157–188.

Kavassalis, P., Bailey, J. P., & Lee, T. Y. (2000). Open-layered networks: the growing importance of market coordination. Decision Support Systems, 28 (1–2), 137–153. doi: 10.1016/s0167-9236(99)00080-9

Kavassalis, P., Spyropoulou, N., Drossos, D., Mitrokostas, E., Gikas, G., & Hatzistamatiou, A. (2003). Mobile permission marketing: framing the market inquiry. International Journal of Electronic Commerce, 8 (1), 55–79.

Kayhan, V. O., McCart, J. A., & Bhattacherjee, A. (2009). An empirical study of cross-listing in online auctions. Journal of Computer Information Systems, 49 (3), 76–80.

Kayhan, V. O., McCart, J. A., & Bhattacherjee, A. (2010). Cross-bidding in simultaneous online auctions: Antecedents and consequences. Information & Management, 47 (7–8), 325–332. doi: 10.1016/j.im.2010.07.001

Keating, B. W., Quazi, A. M., & Kriz, A. (2009). Financial risk and its impact on new purchasing behavior in the online retail setting. Electronic Markets, 19 (4), 237–250. doi: 10.1007/s12525-009-0021-3

Khouja, M., Hadzikadic, M., Rajagopalan, H. K., & Tsay, L. S. (2008). Application of complex adaptive systems to pricing of reproducible information goods. Decision Support Systems, 44 (3), 725–739. doi: 10.1016/j.dss.2007.10.005

Kiang, M. Y., Raghu, T. S., & Shang, K. H. M. (2000). Marketing on the Internet - who can benefit from an online marketing approach? Decision Support Systems, 27 (4), 383–393. doi: 10.1016/s0167-9236(99)00062-7

Kim, B., Barua, A., & Whinston, A. B. (2002). Virtual field experiments for a digital economy: A new research methodology for exploring an information economy. Decision Support Systems, 32 (3), 215–231.

Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile Internet: An empirical investigation. Decision Support Systems, 43 (1), 111–126. doi: 10.1016/j.dss.2005.05.009

Kim, J., Jung, L., Han, K., & Lee, M. (2002). Businesses as buildings: Metrics for the architectural quality of Internet businesses. Information Systems Research, 13 (3), 239–254. doi: 10.1287/isre.13.3.239.79

Kim, J. W., Lee, B. H., Shaw, M. J., Chang, H. L., & Nelson, M. (2001). Application of decision-tree induction techniques to personalized advertisements on Internet storefronts. International Journal of Electronic Commerce, 5 (3), 45–62.

Kim, K., Kim, G. M., & Kil, E. S. (2009). Measuring the compatibility factors in mobile entertainment service adoption. Journal of Computer Information Systems, 50 (1), 141–148.

Kim, M. S., & Ahn, J. H. (2006). Comparison of trust sources of an online market-maker in the e-marketplace: Buyer’s and seller’s perspectives. Journal of Computer Information Systems, 47 (1), 84–94.

Kim, Y. (2005). The effects of buyer and product traits with seller reputation on price premiums in e-auction. Journal of Computer Information Systems, 46 (1), 79–91.

King, R. C., Sen, R., & Xia, M. (2004). Impact of Web-based e-commerce on channel strategy in retailing. International Journal of Electronic Commerce, 8 (3), 103–130.

Ko, I. S., & Leem, C. S. (2004). An Improvement of Response Speed for Electronic Commerce Systems. Information Systems Frontiers, 6 (4), 313–323.

Kocas, C. (2002). Evolution of prices in electronic markets under diffusion of price-comparison shopping. Journal of Management Information Systems, 19 (3), 99–119.

Kocas, C. (2005). A model of Internet pricing under price-comparison shopping. International Journal of Electronic Commerce, 10 (1), 111–134.

Koivumäki, T. (2002). Consumer attitudes and mobile travel portal. Electronic Markets, 12 (1), 47–57.

Komiak, P., Komiak, S. Y. X., & Imhof, M. (2008). Conducting international business at eBay: The Determinants of success of e Stores. Electronic Markets, 18 (2).

Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13 (2), 205–223. doi: 10.1287/isre.13.2.205.83

Kowtha, N. R., & Choon, T. W. I. (2001). Determinants of website development: a study of electronic commerce in Singapore. Information & Management, 39 (3), 227–242.

Kraemer, K. L., & Dedrick, J. (2002). Strategic use of the Internet and e-commerce: Cisco Systems. Journal of Strategic Information Systems, 11 (1), 5–29. doi: 10.1016/s0963-8687(01)00056-7

Kumar, C., Norris, J. B., & Sun, Y. (2009). Location and time do matter: A long tail study of website requests. Decision Support Systems, 47 (4), 500–507. doi: 10.1016/j.dss.2009.04.015

Kumar, K., & Becerra-Fernandez, I. (2007). Interaction technology: Speech act based information technology support for building collaborative relationships and trust. Decision Support Systems, 43 (2), 584–606. doi: 10.1016/j.dss.2005.05.017

Kwan, I. S. Y., Fong, J., & Wong, H. (2005). An e-customer behavior model with online analytical mining for Internet marketing planning. Decision Support Systems, 41 (1), 189–204.

Kwon, O. (2010). A pervasive P3P-based negotiation mechanism for privacy-aware pervasive e-commerce. Decision Support Systems .

Lagrosen, S. (2003). Online service marketing and delivery: the case of Swedish museums. Information Technology & People, 16 (2), 132–156.

Lahiri, A., Dewan, R. M., & Freimer, M. (2010). The disruptive effect of open platforms on markets for wireless services. Journal of Management Information Systems, 27 (3), 81–110.

Le, T. T. (2002). Pathways to leadership for business-to-business electronic marketplaces. Electronic Markets, 12 (2), 112–119.

Le, T. T., Rao, S. S., & Truong, D. (2004). Industry-sponsored marketplaces: a platform for supply chain integration or a vehicle for market aggregation? Electronic Markets, 14 (4), 295–307.

Lee, H. G., Westland, J. C., & Hong, S. (1999). The impact of electronic marketplaces on product prices: an empirical study of AUCNET. International Journal of Electronic Commerce, 4 (2) 45–60.

Lee, J., Podlaseck, M., Schonberg, E., Hoch, R., & Gomory, S. (2000). Understanding merchandising effectiveness of online stores. Electronic Markets, 10 (1), 20–28.

Lee, J. N., Pi, S. M., Kwok, R. C., & Huynh, M. Q. (2003). The contribution of commitment value in Internet commerce: An empirical investigation. Journal of the Association for Information systems, 4 (1), 2.

Lee, K. C., & Kwon, S. (2008). A cognitive map-driven avatar design recommendation DSS and its empirical validity. Decision Support Systems, 45 (3), 461–472. doi: 10.1016/j.dss.2007.06.008

Lee, K. C., & Kwon, S. J. (2006). The use of cognitive maps and case-based reasoning for B2B negotiation. Journal of Management Information Systems, 22 (4), 337–376.

Lee, M. K. O., & Turban, E. (2001). A trust model for consumer Internet shopping. International Journal of Electronic Commerce, 6 , 75–92.

Lee, O. (2002). An action research report on the Korean national digital library. Information & Management, 39 (4), 255–260.

Lee, S., Zufryden, F., & Dreze, X. (2003). A study of consumer switching behavior across Internet portal Web sites. International Journal of Electronic Commerce, 7 (3), 39–63.

Lei-da Chen, S. H., Pandzik, A., Spigarelli, J., & Jesseman, C. (2003). Small business Internet commerce: a case study. Information Resources Management Journal, 16 (3), 17–31.

Leidner, D. E. (1999). Virtual partnerships in support of electronic commerce: the case of TCIS. Journal of Strategic Information Systems, 8 (1), 105–117. doi: 10.1016/s0963-8687(99)00012-8

Levenburg, N. M. (2005). Does size matter? Small firms’ use of E Business tools in the supply chain. Electronic Markets, 15 (2), 94–105.

Levenburg, N. M., & Klein, H. A. (2006). Delivering customer services online: identifying best practices of medium sized enterprises. Information Systems Journal, 16 (2), 135–155.

Li, C. F. (2010). Buy-it now: one dollar auction attractive? Journal of Computer Information Systems, 51 (1), 99–106.

Li, D. H., Browne, G. J., & Wetherbe, J. C. (2006). Why do Internet users stick with a specific Web site? A relationship perspective. International Journal of Electronic Commerce, 10 (4), 105–141. doi: 10.2753/jec1086-4415100404

Li, E. Y., Du, T. C., & Wong, J. W. (2007). Access control in collaborative commerce. Decision Support Systems, 43 (2), 675–685.

Li, E. Y., McLeod, R., & Rogers, J. C. (2001). Marketing information systems in Fortune 500 companies: a longitudinal analysis of 1980, 1990, and 2000. Information & Management, 38 (5), 307–322. doi: 10.1016/s0378-7206(00)00073-2

Li, X., & Hitt, L. (2010). Price effects in online product reviews: An analytical model and empirical analysis. MIS Quarterly, 34 (4), 809–831.

Li, X., Hitt, L. M., & Zhang, Z. J. (2011). Product reviews and competition in markets for repeat purchase products. Journal of Management Information Systems, 27 (4), 9–42.

Li, X., Troutt, M. D., Brandyberry, A., & Wang, T. (2011). Decision factors for the adoption and continued use of Online direct sales channels among SMEs. Journal of the Association for Information systems, 12 (1), 4.

Li, Y. M. (2010). Pricing digital content distribution over heterogeneous channels. Decision Support Systems, 50 (1), 243–257. doi: 10.1016/j.dss.2010.08.027

Li, Y. M., & Lin, C. H. (2009). Pricing schemes for digital content with DRM mechanisms. Decision Support Systems, 47 (4), 528–539. doi: 10.1016/j.dss.2009.05.015

Liang, T. P., & Huang, J. S. (1998). An empirical study on consumer acceptance of products in electronic markets: a transaction cost model. Decision Support Systems, 24 (1), 29–43.

Liang, T. P., & Huang, J. S. (2000). A framework for applying intelligent agents to support electronic trading. Decision Support Systems, 28 (4), 305–317. doi: 10.1016/s0167-9236(99)00098-6

Liang, T. P., Yang, Y. F., Chen, D. N., & Ku, Y. C. (2008). A semantic-expansion approach to personalized knowledge recommendation. Decision Support Systems, 45 (3), 401–412.

Liao, Z. Q., & Cheung, M. T. (2001). Internet-based e-shopping and consumer attitudes: an empirical study. Information & Management, 38 (5), 299–306. doi: 10.1016/s0378-7206(00)00072-0

Liao, Z. Q., & Cheung, M. T. (2002). Internet-based e-banking and consumer attitudes: an empirical study. Information & Management, 39 (4), 283–295. doi: 10.1016/s0378-7206(01)00097-0

Lim, S. H., Lee, S., Hur, Y., & Koh, C. E. (2009). Role of trust in adoption of online auto insurance. Journal of Computer Information Systems, 50 (2), 151–159.

Lin, C. H., & Nguyen, C. (2011). Exploring e-payment adoption in vietnam and taiwan. Journal of Computer Information Systems, 51 (4), 41–52.

Liu, C., Arnett, K. P., Capella, L. M., & Beatty, R. C. (1997). Web sites of the Fortune 500 companies: Facing customers through home pages. Information & Management, 31 (6), 335–345. doi: 10.1016/s0378-7206(97)00001-3

Liu, C., Arnett, K. P., Capella, L. M., & Taylor, R. D. (2001). Key dimensions of Web design quality as related to consumer response. Journal of Computer Information Systems, 42 (1), 70–82.

Lohe, J., & Legner, C. (2010). SOA adoption in business networks: do service-oriented architectures really advance inter-organizational integration? Electronic Markets, 20 (3–4), 181–196. doi: 10.1007/s12525-010-0046-7

López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45 (6), 359–364.

Lu, H., & Lin, J. C. C. (2002). Predicting customer behavior in the market-space: a study of Rayport and Sviokla’s framework. Information & Management, 40 (1), 1–10.

Lu, H. P., & Hsiao, K. L. (2010). The influence of extro/introversion on the intention to pay for social networking sites. Information & Management, 47 (3), 150–157.

Lu, M. T., & Chan, T. S. (1999). The current status of Internet commerce in Hong Kong. Journal of Computer Information Systems, 39 (4), 16–21.

Lu, Y. B., Deng, Z. H., & Wang, B. (2010). Exploring factors affecting Chinese consumers’ usage of short message service for personal communication. Information Systems Journal, 20 (2), 183–208. doi: 10.1111/j.1365-2575.2008.00312.x

Luo, X., & Seyedian, M. (2003). Contextual marketing and customer-orientation strategy for e-commerce: an empirical analysis. International Journal of Electronic Commerce, 8 (2), 95–118.

Ma, Z. M., Pant, G., & Sheng, O. R. L. (2010). Examining organic and sponsored search results: a vendor reliability perspective. Journal of Computer Information Systems, 50 (4), 30–38.

MacInnes, I., Li, Y. F., & Yurcik, W. (2005). Reputation and dispute in eBay transactions. International Journal of Electronic Commerce, 10 (1), 27–54.

Maditinos, D. I., & Theodoridis, K. (2010). Satisfaction determinants in the Greek online shopping context. Information Technology & People, 23 (4), 312–329. doi: 10.1108/09593841011087789

Mai, B., Menon, N. M., & Sarkar, S. (2010). No free lunch: Price premium for privacy seal-bearing vendors. Journal of Management Information Systems, 27 (2), 189–212. doi: 10.2753/mis0742-1222270206

Markopoulos, P. M., Aron, R., & Ungar, L. (2010). Information markets for product attributes: A game theoretic, dual pricing mechanism. Decision Support Systems, 49 (2), 187–199. doi: 10.1016/j.dss.2010.02.005

Martinsons, M. G. (2008). Relationship-based e-commerce: theory and evidence from China. Information Systems Journal, 18 (4), 331–356. doi: 10.1111/j.1365-2575.2008.00302.x

Massey, A. P., Montoya-Weiss, M. M., & Holcom, K. (2001). Re-engineering the customer relationship: leveraging knowledge assets at IBM. Decision Support Systems, 32 (2), 155–170. doi: 10.1016/s0167-9236(01)00108-7

McCarthy, R. V., & Aronson, J. E. (2000). Activating consumer response: A model for web site design strategy. Journal of Computer Information Systems, 41 (2), 2–8.

McKnight, D. H., & Chervany, N. L. (2001). What trust means in e-commerce customer relationships: an interdisciplinary conceptual typology. International Journal of Electronic Commerce, 6 (2), 35–59.

McKnight, D. H., Choudhury, V., & Kacmar, C. (2003). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13 (3), 334–359.

Miller, T. W., & Dickson, P. R. (2001). On-line market research. International Journal of Electronic Commerce, 5 (3), 139–167.

Mithas, S., Jones, J. L., & Mitchell, W. (2008). Buyer intention to use internet-enabled reverse auctions: the role of asset specificity, product specialization, and non-contractibility. MIS Quarterly, 32 (4), 705–724.

Möllenberg, A. (2004). Internet auctions in marketing: the consumer perspective. Electronic Markets, 14 (4), 360–371.

Montazemi, A. R., Siam, J. J., & Esfahanipour, A. (2008). Effect of network relations on the adoption of electronic trading systems. Journal of Management Information Systems, 25 (1), 233–266. doi: 10.2753/mis0742-1222250109

Mort, D. (1998). The impact of the Internet on the European online business information market. Database, 21 (4), 74–78.

Muller-Lankenau, C., Wehmeyer, K., & Klein, S. (2005). Multi-channel strategies: Capturing and exploring diversity in the European retail grocery industry. International Journal of Electronic Commerce, 10 (2), 85–122.

Mun, H. J., Yun, H., Kim, E. A., Hong, J. Y., & Lee, C. C. (2010). Research on factors influencing intention to use DMB using extended IS success model. Information Technology and Management , 1–13.

Murphy, J., Raffa, L., & Mizerski, R. (2003). The use of domain names in e-branding by the world’s top brands. Electronic Markets, 13 (3), 222–232.

Muylle, S., & Basu, A. (2004). Online support for commerce processes and survivability of web retailers. Decision Support Systems, 38 (1), 101–113.

Muylle, S., & Basu, A. (2008). Online support for business processes by electronic intermediaries. Decision Support Systems, 45 (4), 845–857. doi: 10.1016/j.dss.2008.02.005

Muylle, S., Moenaert, R., & Despontin, M. (2004). The conceptualization and empirical validation of web site user satisfaction. Information & Management, 41 (5), 543–560.

Nault, B. R., & Dexter, A. S. (2006). Agent-intermediated electronic markets in international freight transportation. Decision Support Systems, 41 (4), 787–802. doi: 10.1016/j.dss.2004.10.008

Ngai, E., & Wat, F. (2002). A literature review and classification of electronic commerce research. Information & Management, 39 (5), 415–429.

Nikolaeva, R. (2005). Strategic determinants of web site traffic in on-line retailing. International Journal of Electronic Commerce, 9 (4), 113–132.

Novak, J., & Schwabe, G. (2009). Designing for reintermediation in the brick-and-mortar world: Towards the travel agency of the future. Electronic Markets, 19 (1), 15–29. doi: 10.1007/s12525-009-0003-5

Nysveen, H., & Pedersen, P. E. (2004). An exploratory study of customers’ perception of company web sites offering various interactive applications: moderating effects of customers’ Internet experience. Decision Support Systems, 37 (1), 137–150.

O’Connor, G. C., & O’Keefe, B. (1997). Viewing the Web as a marketplace: the case of small companies. Decision Support Systems, 21 (3), 171–183. doi: 10.1016/s0167-9236(97)00027-4

Oh, W., & Lucas, H. C. (2006). Information technology and pricing decisions: Price adjustments in online computer markets. MIS Quarterly, 30 (3), 755–775.

Onur, I., & Tomak, K. (2006). Impact of ending rules in online auctions: The case of Yahoo.com. Decision Support Systems, 42 (3), 1835–1842. doi: 10.1016/j.dss.2006.03.010

Oorni, A. (2003). Consumer search in electronic markets: an experimental analysis of travel services. European Journal of Information Systems, 12 (1), 30–40. doi: 10.1057/palgrave.ejis.3000450

Otim, S., & Grover, V. (2010). E-commerce: a brand name’s curse. Electronic Markets, 20 (2), 147–160. doi: 10.1007/s12525-010-0039-6

Ozdemir, Z. D. (2007). Optimal multi-channel delivery of expertise: An economic analysis. International Journal of Electronic Commerce, 11 (3), 89–105. doi: 10.2753/jec1086-4415110303

Ozdemir, Z. D., Akcura, M. T., & Altinkemer, K. (2006). Second opinions and online consultations. Decision Support Systems, 42 (3), 1747–1758. doi: 10.1016/j.dss.2006.03.011

Ozdemir, Z. D., Altinkemer, K., De, P., & Ozcelik, Y. (2010). Donor-to-nonprofit online marketplace: An economic analysis of the effects on fund-raising. Journal of Management Information Systems, 27 (2), 213–242. doi: 10.2753/mis0742-1222270207

Pagell, R. A. (1997). Internet securities: An emerging service for an emerging niche market. Database, 20 (5), 56-&.

Pant, S., Sim, H. T., & Hsu, C. (2001). A framework for developing Web information systems plans: illustration with Samsung Heavy Industries Co., Ltd. Information & Management, 38 (6), 385–408. doi: 10.1016/s0378-7206(00)00078-1

Parameswaran, M., Stallaert, J., & Whinston, A. B. (2001). A market-based allocation mechanism for the DiffServ framework. Decision Support Systems, 31 (3), 351–361. doi: 10.1016/s0167-9236(00)00143-3

Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11 (4), 125–148.

Patel, N. (2002). Emergent forms of IT governance to support global Ebusiness models. Journal of Information Technology Theory and Application, 4 (2), 5.

Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7 (3), 101–134.

Pedersen, P. E. (2000). Behavioral effects of using software agents for product and merchant brokering. International Journal of Electronic Commerce, 5 (1), 125–141.

Peng, G., Fan, M., & Dey, D. (2011). Impact of network effects and diffusion channels on home computer adoption. Decision Support Systems, 51 (3), 384–393. doi: 10.1016/j.dss.2011.01.004

Phan, D. D. (2003). E-business development for competitive advantages: a case study. Information & Management, 40 (6), 581–590.

Phang, C. W., Kankanhalli, A., Ramakrishnan, K., & Raman, K. S. (2010). Customers preference of online store visit strategies: an investigation of demographic variables. European Journal of Information Systems, 19 (3), 344–358.

Piccoli, G., & Lloyd, R. (2010). Strategic impacts of IT-enabled consumer power: Insight from Internet distribution in the U.S. lodging industry. Information & Management, 47 (7–8), 333–340. doi: 10.1016/j.im.2010.07.002

Pihlstrom, M. (2007). Committed to content provider or mobile channel? Determinants of continuous mobile multimedia service use. Journal of Information Technology Theory and Application, 9 (1), 3.

Poon, S. (2000). Business environment and Internet commerce benefit-a small business perspective. European Journal of Information Systems, 9 (2), 72–81.

Poon, S., & Swatman, P. (1999a). An exploratory study of small business Internet commerce issues. Information & Management, 35 (1), 9–18.

Poon, S., & Swatman, P. (1999b). A longitudinal study of expectations in small business Internet commerce. International Journal of Electronic Commerce, 3 (3), 21–33.

Qu, Z., Zhang, H., & Li, H. Z. (2008). Determinants of online merchant rating: Content analysis of consumer comments about Yahoo merchants. Decision Support Systems, 46 (1), 440–449. doi: 10.1016/j.dss.2008.08.004

Rachlevsky-Reich, B., Ben-Shaul, I., Chan, N. T., Lo, A. W., & Poggio, T. (1999). GEM: A global electronic market system. Information Systems, 24 (6), 495–518. doi: 10.1016/s0306-4379(99)00029-0

Rafaeli, S., & Noy, A. (2002). Online auctions, messaging, communication and social facilitation: a simulation and experimental evidence. European Journal of Information Systems, 11 (3), 196–207. doi: 10.1057/palgrave.ejis.3000434

Raghu, T. S., Kannan, P. K., Rao, H. R., & Whinston, A. B. (2001). Dynamic profiling of consumers for customized offerings over the Internet: a model and analysis. Decision Support Systems, 32 (2), 117–134. doi: 10.1016/s0167-9236(01)00106-3

Ram, S., Park, J., & Lee, D. (1999). Digital libraries for the next millennium: challenges and research directions. Information Systems Frontiers, 1 (1), 75–94.

Ranchhod, A., Zhou, F., & Tinson, J. (2001). Factors influencing marketing effectiveness on the Web. Information Resources Management Journal, 14 (1), 4.

Ranganathan, C., & Ganapathy, S. (2002). Key dimensions of business-to-consumer web sites. Information & Management, 39 (6), 457–465.

Ransbotham, S., & Mitra, S. (2009). Choice and chance: A conceptual model of paths to information security compromise. Information Systems Research, 20 (1), 121–139. doi: 10.1287/isre.1080.0174

Rau, P. L. P., Chen, J. W., & Chen, D. Y. (2006). A study of presentations of mobile web banners for location-based information and entertainment information websites. Behaviour & Information Technology, 25 (3), 253–261. doi: 10.1080/01449290500222009

Regev, O., & Nisan, N. (2000). The POPCORN market. Online markets for computational resources. Decision Support Systems, 28 (1–2), 177–189. doi: 10.1016/s0167-9236(99)00067-6

Riedl, R., Hubert, M., & Kenning, P. (2010). Are there neural gender differences in online trust? an FMRI study on the perceived trustworthiness of ebay offers. MIS Quarterly, 34 (2), 397–428.

Riggins, F. J. (2004). A multichannel model of separating equilibrium in the face of the digital divide. Journal of Management Information Systems, 21 (2), 161–179.

Riquelme, H. (2001). An empirical review of price behaviour on the Internet. Electronic Markets, 11 (4), 263–272.

Robertson, G., Murphy, J., & Purchase, S. (2005). Distance to market: Propinquity across In store and Online food retailing. Electronic Markets, 15 (3), 235–245.

Rohm, A. W., & Pernul, G. (2000). COPS: a model and infrastructure for secure and fair electronic markets. Decision Support Systems, 29 (4), 343–355. doi: 10.1016/s0167-9236(00)00082-8

Romano Jr, N. C., & Fjermestad, J. (2001). Electronic commerce customer relationship management: An assessment of research. International Journal of Electronic Commerce, 6 (2), 61–113.

Rossignoli, C., Carugati, A., & Mola, L. (2009). The strategic mediator: a paradoxical role for a collaborative e-marketplace. Electronic Markets, 19 (1), 55–66. doi: 10.1007/s12525-009-0005-3

Roussos, G., Peterson, D., & Patel, U. (2003). Mobile identity management: An enacted view. International Journal of Electronic Commerce, 8 (1), 81–100.

Rust, R. T., & Lemon, K. N. (2001). E-service and the consumer. International Journal of Electronic Commerce, 5 (3), 85–101.

Ryan, G., & Valverde, M. (2006). Waiting in line for online services: a qualitative study of the user’s perspective. Information Systems Journal, 16 (2), 181–211.

Saban, K. A. (2001). Strategic preparedness: a critical requirement to maximize e-commerce investments. Electronic Markets, 11 (1), 26–36.

SAban, K. A., & Rau, S. E. (2005). The functionality of websites as export marketing channels for small and medium enterprises. Electronic Markets, 15 (2), 128–135.

Sagi, J., Carayannis, E., Dasgupta, S., & Thomas, G. (2004). ICT and business in the New Economy: Globalization and attitudes. Journal of Global Information Management, 12 (3), 44–64.

Sen, R., King, R. C., & Shaw, M. J. (2006). Buyers’ choice of online search strategy and its managerial implications. Journal of Management Information Systems, 23 (1), 211–238. doi: 10.2753/mis0742-1222230107

Shang, R. A., Chen, Y. C., & Shen, L. (2005). Extrinsic versus intrinsic motivations for consumers to shop on-line. Information & Management, 42 (3), 401–413.

Shim, J., Shin, Y. B., & Nottingham, L. (2002). Retailer web site influence on customer shopping: exploratory study on key factors of customer satisfaction. Journal of the Association for Information systems, 3 (1), 3.

Shin, B., & Lee, H. G. (2005). Ubiquitous computing driven business models: A Case of SK Telecom’s financial services. Electronic Markets, 15 (1), 4–12.

Shin, D. H. (2009). A cross-national study of mobile Internet services: A comparison of US and Korean mobile Internet users. Journal of Global Information Management, 17 (4), 29–54. doi: 10.4018/jgim.2009070902

Shmueli, G., Jank, W., Aris, A., Plaisant, C., & Shneiderman, B. (2006). Exploring auction databases through interactive visualization. Decision Support Systems, 42 (3), 1521–1538. doi: 10.1016/j.dss.2006.01.001

Sieber, S., & Sabatier, J. V. (2003). Market bundling strategies in the horizontal portal industry. International Journal of Electronic Commerce, 7 (4), 37–54.

Soh, C., Markus, M. L., & Goh, K. H. (2006). Electronic marketplaces and price transparency: Strategy, information technology, and success. MIS Quarterly, 30 (3), 705–723.

Son, J. Y., Kim, S. S., & Riggins, F. J. (2006). Consumer adoption of net-enabled infomediaries: theoretical explanations and an empirical test. Journal of the Association for Information systems, 7 (1), 18.

Song, J., & Zahedi, F. M. (2006). Internet market strategies: Antecedents and implications. Information & Management, 43 (2), 222–238.

Song, P., Zhang, C., Xu, Y. C., & Huang, L. (2010). Brand extension of online technology products: Evidence from search engine to virtual communities and online news. Decision Support Systems, 49 (1), 91–99.

Spiller, P., & Lohse, G. L. (1997). A classification of Internet retail stores. International Journal of Electronic Commerce, 2 (2), 29–56.

Sripalawat, J., Thongmak, M., & Ngramyarn, A. (2011). M-BANKING in metropolitan Bangkok and a comparison with other countries. Journal of Computer Information Systems, 51 (3), 67–76.

Stafford, M. R., & Stern, B. (2002). Consumer bidding behavior on Internet auction sites. International Journal of Electronic Commerce, 7 (1), 135–150.

Standifird, S. S., Roelofs, M. R., & Durham, Y. (2004). The impact of eBay’s buy-it-now function on bidder Behavior. International Journal of Electronic Commerce, 9 (2), 167–176.

Standing, S., Standing, C., & Love, P. E. D. (2010). A review of research on e-marketplaces 1997–2008. Decision Support Systems, 49 (1), 41–51. doi: 10.1016/j.dss.2009.12.008

Steyaert, J. C. (2004). Measuring the performance of electronic government services. Information & Management, 41 (3), 369–375. doi: 10.1016/s0378-7206(03)00025-9

Student, B. M. D., & Marketing, J. L. C. P. f. (2003). The impact of visiting a brand website on brand personality. Electronic Markets, 13 (3), 210–221.

Stylianou, A. C., Kumar, R. L., & Robbins, S. S. (2005). Pricing on the Internet and in conventional retail channels: A study of over-the-counter pharmaceutical products. International Journal of Electronic Commerce, 10 (1), 135–148.

Subramani, M., & Walden, E. (2001). The impact of e-commerce announcements on the market value of firms. Information Systems Research, 12 (2), 135–154.

Subramaniam, C., & Shaw, M. J. (2002). A study of the value and impact of B2B E-commerce: The case of web-based procurement. International Journal of Electronic Commerce, 6 (4), 19–40.

Susarla, A., & Barua, A. (2011). Contracting efficiency and new firm survival in markets enabled by information technology. Information Systems Research, 22 (2), 306–324. doi: 10.1287/isre.1090.0251

Sutanto, J., Phang, C. W., Tan, C. H., & Lu, X. (2010). Dr. Jekyll vis-a-vis Mr. Hyde: Personality variation between virtual and real worlds. Information & Management, 48 (1), 19–26.

Sutcliffe, A. G. (2000). Requirements analysis for socio-technical system design. Information Systems, 25 (3), 213–233. doi: 10.1016/s0306-4379(00)00016-8

Taha, K., & Elmasri, R. (2010). SPGProfile: Speak group profile. Information Systems, 35 (7), 774–790. doi: 10.1016/j.is.2010.04.001

Talukder, M., & Yeow, P. H. P. (2007). A comparative study of virtual communities in Bangladesh and the USA. Journal of Computer Information Systems, 47 (4), 82–90.

Tan, C. H., Teo, H. H., & Xu, H. (2010). Online auction: the effects of transaction probability and listing price on a seller’s decision-making behavior. Electronic Markets, 20 (1), 67–79. doi: 10.1007/s12525-010-0029-8

Tan, J., Cheng, W. N., & Rogers, W. J. (2002). From telemedicine to e-health: Uncovering new frontiers of biomedical research, clinical applications & public health services delivery. Journal of Computer Information Systems, 42 (5), 7–18.

Tan, M., & Teo, T. S. H. (1999). The diffusion of the Internet in a pro-IT cultural environment: A content analysis of the Singapore experience. Communications of the AIS, 2 (3), 6.

Tang, F. F., & Lu, D. (2001). Pricing patterns in the online CD market: an empirical study. Electronic Markets, 11 (3), 171–185.

Tang, K., Chen, Y. L., & Hu, H. W. (2008). Context-based market basket analysis in a multiple-store environment. Decision Support Systems, 45 (1), 150–163.

Tang, Q., & Cheng, H. (2006). Optimal strategies for a monopoly intermediary in the supply chain of complementary web services. Journal of Management Information Systems, 23 (3), 275–307.

Tang, Z. L., Hu, Y., & Smith, M. D. (2008). Gaining trust through online privacy protection: Self-regulation, mandatory standards, or Caveat Emptor. Journal of Management Information Systems, 24 (4), 153–173. doi: 10.2753/mis0742-1222240406

Teich, J., Wallenius, H., & Wallenius, J. (1999). Multiple-issue auction and market algorithms for the world wide web. Decision Support Systems, 26 (1), 49–66. doi: 10.1016/s0167-9236(99)00016-0

Telang, R., Rajan, U., & Mukhopadhyay, T. (2004). The market structure for Internet search engines. Journal of Management Information Systems, 21 (2), 137–160.

Teo, T. S. H. (2002). Attitudes toward online shopping and the Internet. Behaviour & Information Technology, 21 (4), 259–271. doi: 10.1080/0144929021000018342

Teo, T. S. H. (2006). To buy or not to buy online: adopters and non-adopters of online shopping in Singapore. Behaviour & Information Technology, 25 (6), 497–509.

Teo, T. S. H., & Pian, Y. J. (2003). A contingency perspective on Internet adoption and competitive advantage. European Journal of Information Systems, 12 (2), 78–92. doi: 10.1057/palgrave.ejis.3000448

Teo, T. S. H., & Too, B. L. (2000). Information systems orientation and business use of the Internet: An empirical study. International Journal of Electronic Commerce, 4 (4), 105–130.

Tewari, G., Youll, J., & Maes, P. (2003). Personalized location-based brokering using an agent-based intermediary architecture. Decision Support Systems, 34 (2), 127–137. doi: 10.1016/s0167-9236(02)00076-3

Tsang, M. M., Ho, S. C., & Liang, T. P. (2004). Consumer attitudes toward mobile advertising: An empirical study. International Journal of Electronic Commerce, 8 (3), 65–78.

Tu, Y., & Lu, M. (2006). An experimental and analytical study of on-line digital music sampling strategies. International Journal of Electronic Commerce, 10 (3), 39–70.

Usunier, J. C., Roulin, N., & Ivens, B. S. (2009). Cultural, National, and Industry-Level Differences in B2B Web Site Design and Content. International Journal of Electronic Commerce, 14 (2), 41–88.

Van de Kar, E. V., & den Hengst, M. (2009). Involving users early on in the design process: closing the gap between mobile information services and their users. Electronic Markets, 19 (1), 31–42. doi: 10.1007/s12525-008-0002-y

Van der Heijden, H. (2003). Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information & Management, 40 (6), 541–549.

Van Gigch, J. P. (2000). Do we need to impose more regulation upon the World Wide Web?-A metasystem analysis. Informing Science, 3 (3), 109–116.

Van Heijst, D., Potharst, R., & van Wezel, M. (2008). A support system for predicting eBay end prices. Decision Support Systems, 44 (4), 970–982. doi: 10.1016/j.dss.2007.11.004

Vatanasakdakul, S., D’Ambra, J., & Ramburuth, P. (2010). IT doesn’t fit! The influence of culture on B2B in Thailand. Journal of Global Information Technology Management, 13 (3), 10–38.

Vatanasombut, B., Igbaria, M., Stylianou, A. C., & Rodgers, W. (2008). Information systems continuance intention of web-based applications customers: The case of online banking. Information & Management, 45 (7), 419–428. doi: 10.1016/j.im.2008.03.005

Vedder, R. G., Guynes, C. S., & Vanecek, M. T. (1997). Electronic commerce on the WWW/Internet. Journal of Computer Information Systems, 38 (1), 20–25.

Vellido, A., Lisboa, P. J. G., & Meehan, K. (2000). Quantitative characterization and prediction of on-line purchasing behavior: A latent variable approach. International Journal of Electronic Commerce, 4 (4), 83–104.

Verhoef, P. C., Spring, P. N., Hoekstra, J. C., & Leeflang, P. S. H. (2003). The commercial use of segmentation and predictive modeling techniques for database marketing in the Netherlands. Decision Support Systems, 34 (4), 471–481.

Vijayasarathy, L. R., & Jones, J. M. (2000). Intentions to shop using Internet catalogues: exploring the effects of product types, shopping orientations, and attitudes towards computers. Electronic Markets, 10 (1), 29–38.

Vragov, R., Di Shang, R., & Lang, K. R. (2010). On-line auctions with buy-it-now pricing: A practical design model and experimental evaluation. International Journal of Electronic Commerce, 14 (4), 39–67. doi: 10.2753/jec1086-4415140402

Wakefield, R. L., Wakefield, K. L., Baker, J., & Wang, L. C. (2010). How website socialness leads to website use. European Journal of Information Systems, 20 (1), 118–132.

Walter, Z., Gupta, A., & Su, B. C. (2006). The sources of on-line price dispersion across product types: An integrative view of on-line search costs and price premiums. International Journal of Electronic Commerce, 11 (1), 37–62.

Wan, H. A. (2000). Opportunities to enhance a commercial website. Information & Management, 38 (1), 15–21.

Wang, H. C., & Doong, H. S. (2010). Online customers’ cognitive differences and their impact on the success of recommendation agents. Information & Management, 47 (2), 109–114.

Wang, H. Q. (1997). A conceptual model for virtual markets. Information & Management, 32 (3), 147–161. doi: 10.1016/s0378-7206(97)00017-7

Wang, J. C., & Chiang, M. J. (2009). Social interaction and continuance intention in online auctions: A social capital perspective. Decision Support Systems, 47 (4), 466–476.

Wang, Y. S., Lin, H. H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information Systems Journal, 16 (2), 157–179.

Ward, S. G., & Clark, J. M. (2002). Bidding behavior in on-line auctions: an examination of the eBay Pokemon card market. International Journal of Electronic Commerce, 6 (4), 139–155.

Wattal, S., Telang, R., & Mukhpadhyay, T. (2009). Information personalization in a two-dimensional product differentiation model. Journal of Management Information Systems, 26 (2), 69–95. doi: 10.2753/mis0742-1222260204

Weber, T. A., & Zheng, Z. Q. (2007). A model of search intermediaries and paid referrals. Information Systems Research, 18 (4), 414–436. doi: 10.1287/isre.1070.0139

West, L. A. (2000). Private markets for public goods: Pricing strategies of online database vendors. Journal of Management Information Systems, 17 (1), 59–85.

Whitman, M. E., Perez, J., & Beise, C. (2001). A study of user attitudes toward persistent cookies. Journal of Computer Information Systems, 41 (3), 1–7.

Wilson III, E. J. (2000). Wiring the African economy. Electronic Markets, 10 (2), 80–86.

Wu, C. S., Cheng, F. F., & Yen, D. C. (2008). The atmospheric factors of online storefront environment design: An empirical experiment in Taiwan. Information & Management, 45 (7), 493–498.

Xu, B., Jones, D. R., & Shao, B. (2009). Volunteers’ involvement in online community based software development. Information & Management, 46 (3), 151–158.

Xu, D. J., Liao, S. S., & Li, Q. (2008). Combining empirical experimentation and modeling techniques: A design research approach for personalized mobile advertising applications. Decision Support Systems, 44 (3), 710–724.

Xu, H., Luo, X., Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Systems, 51 (1), 42–52. doi: 10.1016/j.dss.2010.11.017

Xue, L., Ray, G., & Whinston, A. B. (2006). Strategic investment in switching cost: An integrated customer acquisition and retention perspective. International Journal of Electronic Commerce, 11 (1), 7–35. doi: 10.2753/jec1086-4415110101

Yang, H. L., & Chiu, H. K. (2002). Privacy disclosures of Web sites in Taiwan. Journal of Information Technology Theory and Application, 4 (3), 4.

Yang, J., Hu, X., & Zhang, H. (2007). Effects of a reputation feedback system on an online consumer-to-consumer auction market. Decision Support Systems, 44 (1), 93–105. doi: 10.1016/j.dss.2007.03.005

Yen, H. J. R., Hsu, S. H. Y., & Huang, C. Y. (2011). Good Soldiers on the Web: Understanding the Drivers of Participation in Online Communities of Consumption. International Journal of Electronic Commerce, 15 (4), 89–120.

Yen, H. J. R., Li, E. Y., & Niehoff, B. P. (2008). Do organizational citizenship behaviors lead to information system success?: Testing the mediation effects of integration climate and project management. Information & Management, 45 (6), 394–402.

Yeung, W. L., & Lu, M. (2004). Functional characteristics of commercial web sites: a longitudinal study in Hong Kong. Information & Management, 41 (4), 483–495.

Yeung, W. L., & Lu, M. T. (2004). Gaining competitive advantages through a functionality grid for website evaluation. Journal of Computer Information Systems, 44 (4), 67–77.

Yue, W. T., & Chaturvedi, A. (2000). The reward based online shopping community. Electronic Markets, 10 (4), 224–228.

Zhang, D. S. (2004). Web services composition for process management in E-business. Journal of Computer Information Systems, 45 (2), 83–91.

Zhang, H., & Li, H. Z. (2006). Factors affecting payment choices in online auctions: A study of eBay traders. Decision Support Systems, 42 (2), 1076–1088. doi: 10.1016/j.dss.2005.09.003

Zhang, J., Fang, X., & Sheng, O. R. L. (2006). Online consumer search depth: Theories and new findings. Journal of Management Information Systems, 23 (3), 71–95. doi: 10.2753/mis0742-1222230304

Zhang, K. Z. K., Lee, M. K. O., Cheung, C. M. K., & Chen, H. P. (2009). Understanding the role of gender in bloggers’ switching behavior. Decision Support Systems, 47 (4), 540–546. doi: 10.1016/j.dss.2009.05.013

Zhao, J., Wang, S., & Huang, W. V. (2008). A study of B2B e-market in China: E-commerce process perspective. Information & Management, 45 (4), 242–248.

Zhou, M., Dresner, M., & Windle, R. (2009). Revisiting feedback systems: Trust building in digital markets. Information & Management, 46 (5), 279–284. doi: 10.1016/j.im.2009.05.002

Zhu, K., Kraemer, K. L., Gurbaxani, V., & Xu, S. X. (2006). Migration to open-standard interorganizational systems: Network effects, switching costs, and path dependency. MIS Quarterly, 30 , 515–539.

Zhuang, Y. L., & Lederer, A. L. (2006). A resource-based view of electronic commerce. Information & Management, 43 (2), 251–261. doi: 10.1016/j.im.2005.06.006

Zimmerman, R. D., Thomas, R. J., Gan, D. Q., & Murillo-Sanchez, C. (1999). A web-based platform for experimental investigation of electric power auctions. Decision Support Systems, 24 (3–4), 193–205. doi: 10.1016/s0167-9236(98)00083-9

Zviran, M. (2008). User’s perspectives on privacy in web-based applications. Journal of Computer Information Systems, 48 (4), 97–105.

Zwass, V. (2010). Co-Creation: Toward a Taxonomy and an Integrated Research Perspective. International Journal of Electronic Commerce, 15 (1), 11–48. doi: 10.2753/jec1086-4415150101

Appendix B – data sample (22 marketing articles)

Acquisti, A., & Varian, H. R. (2005). Conditioning prices on purchase history. Marketing Science, 24(3), 367–381. doi: 10.1287/mksc.1040.0103

Ancarani, F., & Shankar, V. (2004). Price levels and price dispersion within and across multiple retailer types: Further evidence and extension. Journal of the Academy of Marketing Science, 32(2), 176–187. doi: 10.1177/0092070303261464

Ansari, A., Mela, C. F., & Neslin, S. A. (2008). Customer channel migration. Journal of Marketing Research, 45(1), 60–76. doi: 10.1509/jmkr.45.1.60

Balasubramanian, S. (1998). Mail versus mall: A strategic analysis of competition between direct marketers and conventional retailers. Marketing Science, 17(3), 181–195. doi: 10.1287/mksc.17.3.181

Bodapati, A. V. (2008). Recommendation systems with purchase data. Journal of Marketing Research, 45(1), 77–93. doi: 10.1509/jmkr.45.1.77

Danaher, P. J. (2007). Modeling page views across multiple websites with an application to Internet reach and frequency prediction. Marketing Science, 26(3), 422–437. doi: 10.1287/mksc.1060.0226

Danaher, P. J., Lee, J., & Kerbache, L. (2010). Optimal Internet Media Selection. Marketing Science, 29(2), 336–347. doi: 10.1287/mksc.1090.0507

Fitzsimons, G. J., & Lehmann, D. R. (2004). Reactance to recommendations: When unsolicited advice yields contrary responses. Marketing Science, 23(1), 82–94. doi: 10.1287/mksc.1030.0033

Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545–560. doi: 10.1287/mksc.1040.0071

Hauser, J. R., Urban, G. L., Liberali, G., & Braun, M. (2009). Website morphing. Marketing Science, 28(2), 202–223. doi: 10.1287/mksc.1080.0459

He, C., & Chen, Y. X. (2006). Managing e-Marketplace: A strategic analysis of nonprice advertising. Marketing Science, 25(2), 175–187. doi: 10.1287/mksc.1050.0168

Katona, Z., & Sarvary, M. (2010). The race for sponsored links: Bidding patterns for search advertising. Marketing Science, 29(2), 199–215. doi: 10.1287/mksc.1090.0517

Kozinets, R. V., de Valck, K., Wojnicki, A. C., & Wilner, S. J. S. (2010). Networked Narratives: Understanding Word-of-Mouth Marketing in Online Communities. Journal of Marketing, 74(2), 71–89.

Mayzlin, D. (2006). Promotional chat on the Internet. Marketing Science, 25(2), 155–163. doi: 10.1287/mksc.1050.0137

Moe, W. W., & Trusov, M. (2011). The value of social dynamics in online product ratings forums. Journal of Marketing Research, 48(3), 444–456.

Ofek, E., Katona, Z., & Sarvary, M. (2011). “Bricks and clicks”: The impact of product returns on the strategies of multichannel retailers. Marketing Science, 30(1), 42–60. doi: 10.1287/mksc.1100.0588

Toubia, O., Simester, D. I., Hauser, J. R., & Dahan, E. (2003). Fast polyhedral adaptive conjoint estimation. Marketing Science, 22(3), 273–303. doi: 10.1287/mksc.22.3.273.17743

Wilbur, K. C., & Zhu, Y. (2009). Click fraud. Marketing Science, 28(2), 293–308. doi: 10.1287/mksc.1080.0397

Yoo, W. S., & Lee, E. (2011). Internet channel entry: A strategic analysis of mixed channel structures. Marketing Science, 30(1), 29–41. doi: 10.1287/mksc.1100.0586

Zhang, J., & Krishnamurthi, L. (2004). Customizing promotions in online stores. Marketing Science, 23(4), 561–578. doi: 10.1287/mksc.1040.0055

Zhu, F., & Zhang, X. Q. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133–148.

Zhu, Y., & Wilbur, K. C. (2011). Hybrid advertising auctions. Marketing Science, 30(2), 249–273. doi: 10.1287/mksc.1100.0609

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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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Published : 31 January 2013

Issue Date : September 2013

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

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• 17 Oct 2023

With Subscription Fatigue Setting In, Companies Need to Think Hard About Fees

Subscriptions are available for everything from dental floss to dog toys, but are consumers tiring of monthly fees? Elie Ofek says that subscription revenue can provide stability, but companies need to tread carefully or risk alienating customers.

• 29 Aug 2023
• Cold Call Podcast

As Social Networks Get More Competitive, Which Ones Will Survive?

In early 2023, TikTok reached close to 1 billion users globally, placing it fourth behind the leading social networks: Facebook, YouTube, and Instagram. Meanwhile, competition in the market for videos had intensified. Can all four networks continue to attract audiences and creators? Felix Oberholzer-Gee discusses competition and imitation among social networks in his case “Hey, Insta & YouTube, Are You Watching TikTok?”

• 26 Jun 2023
• Research & Ideas

Want to Leave a Lasting Impression on Customers? Don't Forget the (Proverbial) Fireworks

Some of the most successful customer experiences end with a bang. Julian De Freitas provides three tips to help businesses invest in the kind of memorable moments that will keep customers coming back.

• 31 May 2023

With Predictive Analytics, Companies Can Tap the Ultimate Opportunity: Customers’ Routines

Armed with more data than ever, many companies know what key customers need. But how many know exactly when they need it? An analysis of 2,000 ridesharing commuters by Eva Ascarza and colleagues shows what's possible for companies that can anticipate a customer's routine.

• 30 May 2023

Can AI Predict Whether Shoppers Would Pick Crest Over Colgate?

Is it the end of customer surveys? Definitely not, but research by Ayelet Israeli sheds light on the potential for generative AI to improve market research. But first, businesses will need to learn to harness the technology.

• 24 Apr 2023

What Does It Take to Build as Much Buzz as Booze? Inside the Epic Challenge of Cannabis-Infused Drinks

The market for cannabis products has exploded as more states legalize marijuana. But the path to success is rife with complexity as a case study about the beverage company Cann by Ayelet Israeli illustrates.

• 07 Apr 2023

When Celebrity ‘Crypto-Influencers’ Rake in Cash, Investors Lose Big

Kim Kardashian, Lindsay Lohan, and other entertainers have been accused of promoting crypto products on social media without disclosing conflicts. Research by Joseph Pacelli shows what can happen to eager investors who follow them.

• 10 Feb 2023

COVID-19 Lessons: Social Media Can Nudge More People to Get Vaccinated

Social networks have been criticized for spreading COVID-19 misinformation, but the platforms have also helped public health agencies spread the word on vaccines, says research by Michael Luca and colleagues. What does this mean for the next pandemic?

• 02 Feb 2023

Why We Still Need Twitter: How Social Media Holds Companies Accountable

Remember the viral video of the United passenger being removed from a plane? An analysis of Twitter activity and corporate misconduct by Jonas Heese and Joseph Pacelli reveals the power of social media to uncover questionable situations at companies.

• 06 Dec 2022

Latest Isn’t Always Greatest: Why Product Updates Capture Consumers

Consumers can't pass up a product update—even if there's no improvement. Research by Leslie John, Michael Norton, and Ximena Garcia-Rada illustrates the powerful allure of change. Are we really that naïve?

• 29 Nov 2022

How Much More Would Holiday Shoppers Pay to Wear Something Rare?

Economic worries will make pricing strategy even more critical this holiday season. Research by Chiara Farronato reveals the value that hip consumers see in hard-to-find products. Are companies simply making too many goods?

• 26 Oct 2022

How Paid Promos Take the Shine Off YouTube Stars (and Tips for Better Influencer Marketing)

Influencers aspire to turn "likes" into dollars through brand sponsorships, but these deals can erode their reputations, says research by Shunyuan Zhang. Marketers should seek out authentic voices on YouTube, not necessarily those with the most followers.

• 25 Oct 2022

Is Baseball Ready to Compete for the Next Generation of Fans?

With its slower pace and limited on-field action, major league baseball trails football in the US, basketball, and European soccer in revenue and popularity. Stephen Greyser discusses the state of "America's pastime."

• 18 Oct 2022

When Bias Creeps into AI, Managers Can Stop It by Asking the Right Questions

Even when companies actively try to prevent it, bias can sway algorithms and skew decision-making. Ayelet Israeli and Eva Ascarza offer a new approach to make artificial intelligence more accurate.

• 08 Aug 2022

Building an 'ARMY' of Fans: Marketing Lessons from K-Pop Sensation BTS

Few companies can boast a customer base as loyal and engaged as BTS fans. In a case study, Doug Chung shares what marketers can learn from the boyband's savvy use of social media and authentic connection with listeners.

• 30 Jun 2022

Peloton Changed the Exercise Game. Can the Company Push Through the Pain?

When COVID-19 closed gyms, seemingly everyone rushed to order a Peloton bike and claim a spot on the company's signature leader board. And then things quickly went downhill. A case study by Robert Dolan looks at the tough road the exercise equipment maker faces.

• 30 Nov 2021

TikTok: Super App or Supernova?

TikTok’s parent company, ByteDance, was launched in 2012 around the simple idea of helping users entertain themselves on their smartphones while on the Beijing Subway. By May 2020, TikTok operated in 155 countries and had roughly 1 billion monthly active users, placing it in the top ranks of digital platforms globally. But the app had drawn the attention of competitors, regulators, and politicians, especially in the US, where commercial success was critical to its long-term enterprise value. Would TikTok become the first “Super App” with a global footprint, or did it run the risk of becoming a supernova that shone brightly only for a passing moment? Harvard Business School senior lecturer Jeffrey Rayport discusses these strategic challenges in his case, “TikTok in 2020: Super App or Supernova?” Open for comment; 0 Comments.

• 29 Sep 2021

For Entrepreneurs, Blown Deadlines Can Crush Big Ideas

After a successful launch, entrepreneurs struggle to anticipate the complexities of product upgrades, says research by Andy Wu and Aticus Peterson. They offer three tips to help startups avoid disastrous delays. Open for comment; 0 Comments.

• 13 Jul 2021

Outrage Spreads Faster on Twitter: Evidence from 44 News Outlets

When it comes to social sharing, doom-and-gloom tweets beat sunshine and rainbows, says research by Amit Goldenberg. Is it time to send in the positivity police? Open for comment; 0 Comments.

• 04 Jan 2021
• Working Paper Summaries

The Twofold Effect of Customer Retention in Freemium Settings

Many digital products offer “freemiums”: that is, part of the product for free, often with advertising, and an enhanced customer experience for payment. This research, in a mobile game context, shows the importance of recognizing the short- and long-term effects on customer retention when managing the tradeoffs between free and paid aspects of freemium products.

Electronic Commerce Research

Electronic Commerce Research serves as a catalyst for new research and a forum for disseminating the latest findings in all facets of electronic commerce. The journal’s broad scope encompasses core enabling technologies as well as the implications of these technologies for societies, economies, businesses, and individuals. Readers will find a host of important theoretical and empirical research findings that are leading the way to a better understanding of electronic commerce and its impact.

A sampling of topics as they relate to the internet and electronic commerce include intelligent agents technologies and their impact; economics of electronic commerce; virtual electronic commerce systems; service creation and provisioning; supply chain management through the internet; collaborative learning, gaming, and work; and workflow for electronic commerce applications.

In addition to its regular issues, the journal publishes periodic issues devoted to a single subject area.

Officially cited as: Electron Commer Res

• Serves as a forum for disseminating the latest findings in all facets of electronic commerce
• Explores core enabling technologies as well as the implications of these technologies
• Publishes theoretical and empirical research findings that build a better understanding of electronic commerce
• Features periodic issues devoted to a single subject area

Journal information

• J. Christopher Westland

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Latest issue.

Issue 4, December 2023

Latest articles

Watch your wallet closely with online microloans: a two-stage model for delinquency and default risk management, authors (first, second and last of 4).

• Chenghong Zhang
• Content type: OriginalPaper
• Published: 10 November 2023

Corporate communication during the COVID-19 crisis in a multicultural environment: culture and tweet impact

• Faten F. Kharbat
• Yezen Kannan
• Published: 09 November 2023

The effect of online shopping channel on consumers’ responses and the moderating role of website familiarity

• Lijuan Song
• Published: 07 November 2023

The impact of multi-type online advertising on the consumer engagement transition

• Baixue Chen
• Published: 06 November 2023

An interaction model among enterprise and government actions and public opinion dissemination in negative events

• Xiaoli Wang
• Shuqin Chen
• Published: 04 November 2023

Journal updates

Call for papers on emerging advances in deep learning and computer vision for visual data search and mining in e-commerce.

Learn more about the upcoming Special Issue on Emerging Advances in Deep Learning and Computer Vision for Visual Data Search and Mining in E-commerce

Call for Papers on Designing Virtual Electronic Commerce Systems for Urban Environments

Learn more about the upcoming Special Issue on Designing Virtual Electronic Commerce Systems for Urban Environments

Call for Papers on Online Grocery Shopping – Current and Future Challenges and Opportunities

Learn more about the upcoming Special Issue on Online Grocery Shopping and submit your paper.

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Electronic Commerce Research is indexed by Scopus and has a CiteScore of 7.2 for 2022.

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The Active Role of the Natural Rate of Unemployment during Cyclical Recoveries

We propose that the natural rate of unemployment has an active role in the business cycle, in contrast to the prevailing view that the rate is essentially constant. We demonstrate that this tendency to treat the natural rate as near-constant would explain the surprisingly low slope of the Phillips curve. We show that the natural rate closely tracked the actual rate during the long recovery that began in 2009 and ended in 2020. We explain how the common finding of research in the Phillips-curve framework of low---often extremely low---response of inflation to unemployment could be the result of fairly close tracking of the natural rate and the actual rate in recoveries. Our interpretation of the data contrasts to that of most Phillips-curve studies, that conclude that inflation has little relation to unemployment. We suggest that the flat Phillips curve is an illusion caused by assuming that the natural rate of unemployment has little or no movement during recoveries.

We thank Jordi Gali for providing an extended version of his estimates of the natural rate of unemployment. We thank, without implicating, Yuriy Gorodnichenko, Robert Hetzel and Jeff Lacker for insightful discussions of an earlier version. We thank Brigid C. Meisenbacher for excellent research assistance. Hall's research was supported financially by the Hoover Institution. Neither author has any conflict to disclose. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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• Currently reading: Money market funds spring a leak after year of record inflows
• ‘Higher for longer’ fears fade at first sign of a slowdown
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• Mortgage-backed securities market waits for interest rate clouds to lift
• Floating-rate exposure set to cause more balance-sheet pain
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Money market funds spring a leak after year of record inflows

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A sharp rise in interest rates has cranked up mortgage payments for consumers and escalated borrowing costs for companies around the world over the past 19 months. But it has also lifted the available yields on money market funds to their highest level in years — prompting investors to pour a record $1tn into the asset class since January.

The cascade this year, concentrated mainly in the US, has come in stages. Initially, it was spurred by the Federal Reserve firing the starting gun on rate rises — translating into much improved returns on money market funds, which typically hold short-dated assets, such as government debt. The inflows then accelerated in March and April as fears over the health of the banking sector pulled some investors away from their traditional deposit accounts.

However, even as these concerns eased, the inflows into money market funds broadly persisted, fuelled by expectations that the Fed will keep borrowing costs “ higher for longer ” before starting to cut them. Citing figures from data provider EPFR, strategists at Bank of America Securities now project record full-year fund inflows of $1.3tn.

This enthusiasm for money market funds has been driven by both institutional and private investors.

“Retail investors have been extremely attracted to the 5 per cent yield that they can get on a money-market fund and are putting more and more money into them,” says Shelly Antoniewicz, deputy chief economist at the Investment Company Institute, an association for investment funds, noting that inflows from this cohort were “split almost evenly between government and prime” funds.

Prime funds offer an even higher premium than Treasury-focused vehicles, because they also hold banks’ commercial paper, rather than purely ultra-safe Treasury bills.

But, despite overall inflows in the year to date, October marked a partial reversal of the tide, with $36bn leaving US money market funds on a net basis. That marked the biggest monthly decline since April 2022.

In the seven days to October 18 alone, more than $100bn was pulled out of US money market funds — the biggest weekly outflow on record, based on EPFR data going back to 2007.

But inflows picked up again in subsequent days, with $66bn coming in over the week to November 1. Analysts and investors concur that it is too early to draw firm conclusions about the drivers behind the earlier outflows, or view them as indicative of future moves.

Still, early signs of an outward trickle have sparked questions over the competitiveness of cash-based vehicles as higher interest rates start to drag up the yields on longer-dated assets. It has also fuelled debate over the risk of a small leak from money market funds turning into a torrent.

The outflows in October stemmed primarily from institutional investors, Antoniewicz notes. One likely reason for the withdrawals, she suggests, was that extended payments for US corporate taxes came due in mid-October. Many organisations use money market funds as a place to park their operating cash.

However, Elyas Galou, an investment strategist at BofA Securities, notes that the withdrawal was “quite unusual”.

“Typically, there is a seasonality in money market fund flows,” he points out, meaning that “at the end of each quarter, we may record very large inflows or outflows”. But this “was not an end-of-quarter flow”. While “the data is very volatile” and the drivers of the outflows are still unclear, Galou thinks it was possible that investors were starting to reallocate some of their funds away from money market vehicles.

Short-term debt has been particularly appealing since the Fed started raising rates because of the unusual scenario of an “inverted yield curve” — whereby bonds at the front-end of the Treasury curve yield more than longer-dated assets.

More recently, though, pressure on longer-dated government paper took the benchmark 10-year US government bond yield above 5 per cent in October, reaching its highest point since 2007.

John Tobin, chief investment officer at Dreyfus, notes that “we’ve certainly seen more sophisticated investors — hedge funds, real money players — extending duration on their own by taking money from the front end, either out of bank deposits or money market funds, and going into the direct markets, like Treasury bills or agency debt”, with a view to locking in yields for some time.

In response, Dreyfus itself is “extending duration to hold on to yields for as long as we can” within money market funds, explains Tobin, so that “money funds’ yields are going to look great versus direct markets in a rate-cutting environment”.

We’ve certainly seen more sophisticated investors . . . going into the direct markets, like Treasury bills or agency debt John Tobin, chief investment officer at Dreyfus

However, at the same time, there is still a rivalry between money market funds themselves — despite the vehicles all broadly offering much better returns than they did a couple of years ago.

“We thought there’d be more flexibility now that we’re paying well over 500 basis points, but that competitiveness is still there,” says Tobin.

Still, he adds, as far as broader reallocation goes, many companies need to keep cash in the vehicles for funding purposes — and such cash is “very risk averse”. “Things like equities would not be an option for them,” he says.

Deposit rates could pose more of a challenge to money market funds if banks remain aggressive in terms of what they can pay out to investors. But, overall, Tobin says the risk is “very, very low” of “money pouring out” or a “drawdown in the money market fund universe”.

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Digital Renaissance: NVIDIA Neuralangelo Research Reconstructs 3D Scenes

Editor’s note: Neuralangelo is now available on GitHub . 

Neuralangelo, a new AI model by NVIDIA Research for 3D reconstruction using neural networks, turns 2D video clips into detailed 3D structures — generating lifelike virtual replicas of buildings, sculptures and other real-world objects.

Like Michelangelo sculpting stunning, life-like visions from blocks of marble, Neuralangelo generates 3D structures with intricate details and textures. Creative professionals can then import these 3D objects into design applications, editing them further for use in art, video game development, robotics and industrial digital twins .

Neuralangelo’s ability to translate the textures of complex materials — including roof shingles, panes of glass and smooth marble — from 2D videos to 3D assets significantly surpasses prior methods. The high fidelity makes its 3D reconstructions easier for developers and creative professionals to rapidly create usable virtual objects for their projects using footage captured by smartphones.

“The 3D reconstruction capabilities Neuralangelo offers will be a huge benefit to creators, helping them recreate the real world in the digital world,” said Ming-Yu Liu, senior director of research and co-author on the paper. “This tool will eventually enable developers to import detailed objects — whether small statues or massive buildings — into virtual environments for video games or industrial digital twins.”

In a demo, NVIDIA researchers showcased how the model could recreate objects as iconic as Michelangelo’s David and as commonplace as a flatbed truck. Neuralangelo can also reconstruct building interiors and exteriors — demonstrated with a detailed 3D model of the park at NVIDIA’s Bay Area campus.

Neural Rendering Model Sees in 3D

Prior AI models to reconstruct 3D scenes have struggled to accurately capture repetitive texture patterns, homogenous colors and strong color variations. Neuralangelo adopts instant neural graphics primitives, the technology behind NVIDIA Instant NeRF , to help capture these finer details.

Using a 2D video of an object or scene filmed from various angles, the model selects several frames that capture different viewpoints — like an artist considering a subject from multiple sides to get a sense of depth, size and shape.

Once it’s determined the camera position of each frame, Neuralangelo’s AI creates a rough 3D representation of the scene, like a sculptor starting to chisel the subject’s shape.

The model then optimizes the render to sharpen the details, just as a sculptor painstakingly hews stone to mimic the texture of fabric or a human figure.

The final result is a 3D object or large-scale scene that can be used in virtual reality applications, digital twins or robotics development.

Find NVIDIA Research at CVPR, June 18-22

Neuralangelo is one of nearly 30 projects by NVIDIA Research to be presented at the Conference on Computer Vision and Pattern Recognition (CVPR), taking place June 18-22 in Vancouver. The papers span topics including pose estimation, 3D reconstruction and video generation.

One of these projects, DiffCollage , is a diffusion method that creates large-scale content — including long landscape orientation, 360-degree panorama and looped-motion images. When fed a training dataset of images with a standard aspect ratio, DiffCollage treats these smaller images as sections of a larger visual — like pieces of a collage. This enables diffusion models to generate cohesive-looking large content without being trained on images of the same scale.

The technique can also transform text prompts into video sequences, demonstrated using a pretrained diffusion model that captures human motion:

Learn more about NVIDIA Research at CVPR .

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United flight makes emergency landing at SFO after reported bomb threat

• Economy & Politics

Associated Press

New white house initiative on women’s-health research to be led by first lady jill biden, president joe biden said he’s long been a believer in the ‘power of research’ to help save lives and get high-quality healthcare to the people who need it, president joe biden and first lady jill biden embrace in the oval office after the signing monday of a presidential memorandum to establish the white house initiative on women’s health research. also shown are office of management and budget director shalanda young, left, and women’s alzheimer’s movement founder maria shriver..

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WASHINGTON (AP) — The Biden administration on Monday announced a White House initiative to improve how the federal government approaches and funds research into the health of women, who make up more than half of the U.S. population but remain understudied and underrepresented in health research.

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