1. NCCU Academic Hub
  2. Publications
  3. College of Commerce
  4. Theses
  5. Item

Publications-Theses

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 線上決策輔助是否改變傳統上消費者之決策漏斗
Do online decision aids change the traditional decision funnel for customers
作者 蘇曉淳
Su, Annie
貢獻者 吳文傑
Wu, Jack
蘇曉淳
Su, Annie
關鍵詞 線上決策
決策漏斗
消費者
Online decision
Decision funnel
Customers
日期 2017
上傳時間 24-Jul-2017 12:08:19 (UTC+8)
摘要 The goal of this study was to build a more holistic and comprehensive look of the effects of search and decision tools (collectively known as decision aids) on the traditional consumer decision process. Specifically, how it affects the information search and alternative evaluation stages. It combined multiple models and concepts from different areas of consumer decision behavior, decision support systems, technology acceptance and task-technology fit theory. It explores how consumers use five different decision aids that are commonly found in today’s marketplace: consumer reviews, social media and electronic-word-of-mouth, comparison matrices, filter agents, and virtual assistants. The effects of these different decision aids were compared in both the information search stage and alternative evaluation stage.

In information search, a 5x2 within-subject factorial study was used to determine the effects of decision aids over time (present vs. ten years ago). Two-way repeated ANOVA found that the effects of decision aids in terms of perceived usage across all decision aids have increased from that of ten years ago. Also, consistent with task-technology fit theory usage between each decision aid differed based on how well the decision aid’s capabilities matched the stage’s need.

In the alternative evaluation stage, three treatments were manipulated: decision aids, task complexity (high vs. low) and step within the alternative evaluation stage of the consumer decision process (screening vs. evaluation step) in a 5x2x2 within-subject factorial design. The treatments were compared by measuring its effects on four dependent variables proposed in technology acceptance literature: perceived ease of use, perceived usefulness, trusting beliefs and intention to use. Three-way repeated ANOVA showed that consumers rely on a two-step process when faced with high task complexity, screening out alternatives based on a simple non-compensatory rule before more detailed evaluation of the remaining alternatives are evaluated. The results were also consistent with task-fit theory with decision aids differing based on how well their capabilities matched each stage. The study however couldn’t provide definitive proof of differences in the two steps within the alternative evaluation as the significance of the results varied.
參考文獻 Alba, Joseph, John Lynch, Barton Weitz, Chris Janiszewski, Richard Lutz, Alan Sawyer, and Stacy Wood (1997), “Interactive Home Shopping. Consumer, Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces,” Journal of Marketing, 61 (3), 38.
Anderson, Christopher J. (2003), “The psychology of doing nothing. Forms of decision avoidance result from reason and emotion,” Psychological Bulletin, 129 (1), 139–67.
Arnold Kamis, Marios Koufaris, and Tziporah Stern (2008), “Using an Attribute-Based Decision Support System for User-Customized Products Online: An Experimental Investigation,” MIS Quarterly, 32 (1), 159–77.
Baumol, William J. and Edward A. Ide (1956), “Variety in Retailing,” Management Science, 3 (1), 93–101.
Benbasat, Izak and Weiquan Wang (2005), “Trust In and Adoption of Online Recommendation Agents,” Journal of the Association for Information Systems, 6 (3).
Benlian, Alexander, Ryad Titah, and Thomas Hess (2012), “Differential Effects of Provider Recommendations and Consumer Reviews in E-Commerce Transactions. An Experimental Study,” Journal of Management Information Systems, 29 (1), 237–72.
Bo Xiao and Izak Benbasat (2007), “E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact,” MIS Quarterly, 31 (1), 137–209.
Botti, Simona and Sheena S. Lyengar (2004), “The psychological pleasure and pain of choosing: when people prefer choosing at the cost of subsequent outcome satisfaction,” Journal of personality and social psychology, 87 (3), 312–26.
Broniarczyk, Susan M. and Jill G. Griffin (2014), “Decision Difficulty in the Age of Consumer Empowerment,” Journal of Consumer Psychology, 24 (4), 608–25.
Bruyn, Arnaud de, John C. Liechty, Eelko K. R. E. Huizingh, and Gary L. Lilien (2008), “Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids,” Marketing Science, 27 (3), 443–60.
Chau, Patrick Y. (2015), “An Empirical Assessment of a Modified Technology Acceptance Model,” Journal of Management Information Systems, 13 (2), 185–204.
Childers, Terry L., Christopher L. Carr, Joann Peck, and Stephen Carson (2001), “Hedonic and utilitarian motivations for online retail shopping behavior,” Journal of Retailing, 77 (4), 511–35.
Davis, Fred D. (1989), “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, 13 (3), 319.
———, Richard P. Bagozzi, and Paul R. Warshaw (1992), “Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1,” Journal of Applied Social Psychology, 22 (14), 1111–32.
Day, George S., Allan D. Shocker, and Rajendra K. Srivastava (1979), “Customer-Oriented Approaches to Identifying Product-Markets,” Journal of Marketing, 43 (4), 8.
DeNale, Rebecca and Deanna Weidenhamer (2017), “U.S. Census Bureau News Quarterly Retail e-Commerce Sales: 4th Quarter 2016,” (Accessed May 4, 2017), [available at https://www.census.gov/retail/ecommerce/historic_releases.html]
Diehl, Kristin (2005), “When Two Rights Make a Wrong. Searching Too Much in Ordered Environments,” Journal of Marketing Research, 42 (3), 313–22.
———, Laura J. Kornish, and John G. Lynch (2003), “Smart Agents. When Lower Search Costs for Quality Information Increase Price Sensitivity,” Journal of Consumer Research, 30 (1), 56–71.
Duan, W., B. Gu, and A. WHINSTON (2008), “The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry,” Journal of Retailing, 84 (2), 233–42.
Edwards, W. and B. Fasolo (2001), “Decision technology,” Annual review of psychology, 52, 581–606.
Ellison, G. and D. Fudenberg (1995), “Word-of-Mouth Communication and Social Learning,” The Quarterly Journal of Economics, 110 (1), 93–125.
Greenwood, Shannon, Andrew Perrin and Maeve Duggan (2016), “Social Media Update 2016,” (Accessed May 15, 2017), [available at http://www.pewinternet.org/2016/11/11/social-media-update-2016/]
Gilbride, Timothy J. and Greg M. Allenby (2004), “A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules,” Marketing Science, 23 (3), 391–406.
Goodhue, Dale L. and Ronald L. Thompson (1995), “Task-Technology Fit and Individual Performance,” MIS Quarterly, 19 (2), 213.
Häubl, Gerald and Valerie Trifts (2000), “Consumer Decision Making in Online Shopping Environments. The Effects of Interactive Decision Aids,” Marketing Science, 19 (1), 4–21.
Hoch, Stephen J. and David A. Schkade (1996), “A Psychological Approach to Decision Support Systems,” Management Science, 42 (1), 51–64.
Hoffman, Donna L. and Thomas P. Novak (1996), “Marketing in Hypermedia Computer-Mediated Environments. Conceptual Foundations,” Journal of Marketing, 60 (3), 50.
Jacoby, Jacob, Donald E. Speller, and Carol A. Kohn (1974), “Brand Choice Behavior as a Function of Information Load,” Journal of Marketing Research, 11 (1), 63.
Johnson, Eric J., Wendy W. Moe, Peter S. Fader, Steven Bellman, and Gerald L. Lohse (2004), “On the Depth and Dynamics of Online Search Behavior,” Management Science, 50 (3), 299–308.
Kasper, George M. (1996), “A Theory of Decision Support System Design for User Calibration,” Information Systems Research, 7 (2), 215–32.
Kim, Jiyeon and Sandra Forsythe (2008), “Adoption of Virtual Try-on technology for online apparel shopping,” Journal of Interactive Marketing, 22 (2), 45–59.
Kollat, David T., James F. Engel, and Roger D. Blackwell (1970), “Current Problems in Consumer Behavior Research,” Journal of Marketing Research, 7 (3), 327.
Komiak, Sherrie Y. X. and Izak Benbasat (2006), “The Effects of Personalizaion and Familiarity on Trust and Adoption of Recommendation Agents,” MIS Q, 30 (4), 941–60.
Koufaris, Marios (2002), “Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior,” Information Systems Research, 13 (2), 205–23.
Laroche, Michel, Nicolas Papadopoulos, Louise A. Heslop, and Mehdi Mourali (2005), “The influence of country image structure on consumer evaluations of foreign products,” International Marketing Review, 22 (1), 96–115.
Lurie, Nicholas H. and Na Wen (2014), “Simple Decision Aids and Consumer Decision Making,” Journal of Retailing, 90 (4), 511–23.
Malhotra, Naresh K. (1982), “Multi-stage information processing behavior. An experimental investigation,” Journal of the Academy of Marketing Science, 10 (1-2), 54–71.
Markus, Hazel R. and Barry Schwartz (2010), “Does Choice Mean Freedom and Well-Being?,” Journal of Consumer Research, 37 (2), 344–55.
Murray, Kyle B. and Gerald Häubl (2009), “Personalization without Interrogation. Towards more Effective Interactions between Consumers and Feature-Based Recommendation Agents,” Journal of Interactive Marketing, 23 (2), 138–46.
Olson, Erik L. and Robert E. Widing (2002), “Are interactive decision aids better than passive decision aids? A comparison with implications for information providers on the internet,” Journal of Interactive Marketing, 16 (2), 22–33.
Paul A. Pavlou (2003), “Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model,” International Journal of Electronic Commerce, 7 (3), 101–34.
Payne, John W. (1976), “Task complexity and contingent processing in decision making. An information search and protocol analysis,” Organizational Behavior and Human Performance, 16 (2), 366–87.
Reibstein, David J., Stuart A. Youngblood, and Howard L. Fromkin (1975), “Number of choices and perceived decision freedom as a determinant of satisfaction and consumer behavior,” Journal of Applied Psychology, 60 (4), 434–37.
Reynolds, Kristy E. and Sharon E. Beatty (1999), “Customer benefits and company consequences of customer-salesperson relationships in retailing,” Journal of Retailing, 75 (1), 11–32.
Rogers, Everett M. (op. 1995), “Diffusion of Innovations: Modifications of a Model for Telecommunications,” in Die Diffusion von Innovationen in der Telekommunikation. Schriftenreihe des wissenschaftlichen Instituts für Kommunikationsdienste, Vol. 17, Matthias-Wolfgang Stoetzer, ed. Berlin: Springer, 25–38.
Shugan, Steven M. (1980), “The Cost of Thinking,” Journal of Consumer Research, 7 (2), 99.
Silverman, Barry G., Mintu Bachann, and Khaled Al-Akharas (2001), “Implications of buyer decision theory for design of e-commerce websites,” International Journal of Human-Computer Studies, 55 (5), 815–44.
Simon, Herbert A. (1955), “A Behavioral Model of Rational Choice,” The Quarterly Journal of Economics, 69 (1), 99.
Song, Jaeki, Donald Jones, and Naveen Gudigantala (2007), “The effects of incorporating compensatory choice strategies in Web-based consumer decision support systems,” Decision Support Systems, 43 (2), 359–74.
Sposito, V. A., M. L. Hand, and Bradley Skarpness (2007), “On the efficiency of using the sample kurtosis in selecting optimal l p estimators,” Communications in Statistics - Simulation and Computation, 12 (3), 265–72.
Stoetzer, Matthias-Wolfgang, ed. (op. 1995), Die Diffusion von Innovationen in der Telekommunikation. Schriftenreihe des wissenschaftlichen Instituts für Kommunikationsdienste, Vol. 17. Berlin: Springer.
Swaminathan, Vanitha (2003), “The Impact of Recommendation Agents on Consumer Evaluation and Choice: The Moderating Role of Category Risk, Product Complexity, and Consumer Knowledge,” Journal of Consumer Psychology, 13 (1-2), 93–101.
Tan, Chuan-Hoo, Hock-Hai Teo, and Izak Benbasat (2010), “Assessing Screening and Evaluation Decision Support Systems. A Resource-Matching Approach,” Information Systems Research, 21 (2), 305–26.
Teo, Thompson S. and Yon D. Yeong (2003), “Assessing the consumer decision process in the digital marketplace,” Omega, 31 (5), 349–63.
Todd, Peter and Izak Benbasat (1999), “Evaluating the Impact of DSS, Cognitive Effort, and Incentives on Strategy Selection,” Information Systems Research, 10 (4), 356–74.
——— and ——— (2000), “Inducing compensatory information processing through decision aids that facilitate effort reduction. An experimental assessment,” Journal of Behavioral Decision Making, 13 (1), 91–106.
van der Heijden, Hans (2004), “User Acceptance of Hedonic Information Systems,” MIS Quarterly, 28 (4), 695–704.
van Zee, Emily H., Thaddeus F. Paluchowski, and Lee R. Beach (1992), “The effects of screening and task partitioning upon evaluations of decision options,” Journal of Behavioral Decision Making, 5 (1), 1–19.
Venkatraman, Meera P. and Linda L. Price (1990), “Differentiating between cognitive and sensory innovativeness,” Journal of Business Research, 20 (4), 293–315.
Xiao, Bo and Izak Benbasat (2007), “E-commerce product recommendation agents: use, characteristics, and impact,” MIS Quarterly, 31 (1), 137–209.
Zhang, Jing and En Mao (2016), “From Online Motivations to Ad Clicks and to Behavioral Intentions. An Empirical Study of Consumer Response to Social Media Advertising,” Psychology & Marketing, 33 (3), 155–64.
Zhang, Ping (2013), Toward a Positive Design Theory: Principles for Designing Motivating Information and Communication Technology.
描述 碩士
國立政治大學
國際經營管理英語碩士學位學程(IMBA)
103933039
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0103933039
資料類型 thesis
dc.contributor.advisor 吳文傑zh_TW
dc.contributor.advisor Wu, Jacken_US
dc.contributor.author (Authors) 蘇曉淳zh_TW
dc.contributor.author (Authors) Su, Annieen_US
dc.creator (作者) 蘇曉淳zh_TW
dc.creator (作者) Su, Annieen_US
dc.date (日期) 2017en_US
dc.date.accessioned 24-Jul-2017 12:08:19 (UTC+8)-
dc.date.available 24-Jul-2017 12:08:19 (UTC+8)-
dc.date.issued (上傳時間) 24-Jul-2017 12:08:19 (UTC+8)-
dc.identifier (Other Identifiers) G0103933039en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/111344-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際經營管理英語碩士學位學程(IMBA)zh_TW
dc.description (描述) 103933039zh_TW
dc.description.abstract (摘要) The goal of this study was to build a more holistic and comprehensive look of the effects of search and decision tools (collectively known as decision aids) on the traditional consumer decision process. Specifically, how it affects the information search and alternative evaluation stages. It combined multiple models and concepts from different areas of consumer decision behavior, decision support systems, technology acceptance and task-technology fit theory. It explores how consumers use five different decision aids that are commonly found in today’s marketplace: consumer reviews, social media and electronic-word-of-mouth, comparison matrices, filter agents, and virtual assistants. The effects of these different decision aids were compared in both the information search stage and alternative evaluation stage.

In information search, a 5x2 within-subject factorial study was used to determine the effects of decision aids over time (present vs. ten years ago). Two-way repeated ANOVA found that the effects of decision aids in terms of perceived usage across all decision aids have increased from that of ten years ago. Also, consistent with task-technology fit theory usage between each decision aid differed based on how well the decision aid’s capabilities matched the stage’s need.

In the alternative evaluation stage, three treatments were manipulated: decision aids, task complexity (high vs. low) and step within the alternative evaluation stage of the consumer decision process (screening vs. evaluation step) in a 5x2x2 within-subject factorial design. The treatments were compared by measuring its effects on four dependent variables proposed in technology acceptance literature: perceived ease of use, perceived usefulness, trusting beliefs and intention to use. Three-way repeated ANOVA showed that consumers rely on a two-step process when faced with high task complexity, screening out alternatives based on a simple non-compensatory rule before more detailed evaluation of the remaining alternatives are evaluated. The results were also consistent with task-fit theory with decision aids differing based on how well their capabilities matched each stage. The study however couldn’t provide definitive proof of differences in the two steps within the alternative evaluation as the significance of the results varied.		en_US
dc.description.tableofcontents Abstract ii
List of Figures iii
List of Tables iii
List of abbreviations iv
1. The Necessity of Decision Aids in a Choice Saturated Environment 2
2. Theoretical and Conceptual Foundation 4
2.1 Search and Decision Tools: Decision Aids 4
2.1.1 Consumer Reviews 6
2.1.2 Social Media and Electronic Word of Mouth 7
2.1.3 Comparison Matrix 8
2.1.4 Filtering Agents 9
2.1.5 Virtual Assistants 10
2.2 Traditional Consumer Decision Funnel 10
2.3 Decision Strategies 13
2.3.1 Two-Stage Decision Making Process 13
2.3.2 Compensatory, vs. Non-compensatory Strategies 14
2.4 Technology Acceptance Model 16
3. Research Framework 17
3.1 Research Model 17
3.2 Research Hypotheses 19
3.2.1 Hypotheses on the Information Search Stage 20
3.2.2 Hypotheses on the Alternative Evaluation Stage 21
4. Research Methodology 24
4.1 Pretests 25
4.1 Final Study 26
5. Results 27
5.1 Demographic Profile of Respondents 27
5.2 Control and Manipulation Checks 28
5.3 Search stage 29
5.4 Alternative Evaluation Stage 30
5.5 Virtual Assistants Results 38
6. Discussion 38
6.1 Summary 38
6.2 Implications of Findings 41
6.2.1 Theoretical Implications 41
6.2.2 Practical Implications 43
6.3 Limitations and Research Outlook 44
6.3.1 Limitations 44
6.3.2 Research Outlook 46
Appendix A: Final GoogleForms Online Survey 47
Appendix B: List of Questions in Post-Survey Questionnaire for Pretesting 79
Appendix C: Simple Effect Analysis Results in the Alternative Evaluation Stage 80
Bibliography 90		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103933039en_US
dc.subject (關鍵詞) 線上決策zh_TW
dc.subject (關鍵詞) 決策漏斗zh_TW
dc.subject (關鍵詞) 消費者zh_TW
dc.subject (關鍵詞) Online decisionen_US
dc.subject (關鍵詞) Decision funnelen_US
dc.subject (關鍵詞) Customersen_US
dc.title (題名) 線上決策輔助是否改變傳統上消費者之決策漏斗zh_TW
dc.title (題名) Do online decision aids change the traditional decision funnel for customersen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Alba, Joseph, John Lynch, Barton Weitz, Chris Janiszewski, Richard Lutz, Alan Sawyer, and Stacy Wood (1997), “Interactive Home Shopping. Consumer, Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces,” Journal of Marketing, 61 (3), 38.
Anderson, Christopher J. (2003), “The psychology of doing nothing. Forms of decision avoidance result from reason and emotion,” Psychological Bulletin, 129 (1), 139–67.
Arnold Kamis, Marios Koufaris, and Tziporah Stern (2008), “Using an Attribute-Based Decision Support System for User-Customized Products Online: An Experimental Investigation,” MIS Quarterly, 32 (1), 159–77.
Baumol, William J. and Edward A. Ide (1956), “Variety in Retailing,” Management Science, 3 (1), 93–101.
Benbasat, Izak and Weiquan Wang (2005), “Trust In and Adoption of Online Recommendation Agents,” Journal of the Association for Information Systems, 6 (3).
Benlian, Alexander, Ryad Titah, and Thomas Hess (2012), “Differential Effects of Provider Recommendations and Consumer Reviews in E-Commerce Transactions. An Experimental Study,” Journal of Management Information Systems, 29 (1), 237–72.
Bo Xiao and Izak Benbasat (2007), “E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact,” MIS Quarterly, 31 (1), 137–209.
Botti, Simona and Sheena S. Lyengar (2004), “The psychological pleasure and pain of choosing: when people prefer choosing at the cost of subsequent outcome satisfaction,” Journal of personality and social psychology, 87 (3), 312–26.
Broniarczyk, Susan M. and Jill G. Griffin (2014), “Decision Difficulty in the Age of Consumer Empowerment,” Journal of Consumer Psychology, 24 (4), 608–25.
Bruyn, Arnaud de, John C. Liechty, Eelko K. R. E. Huizingh, and Gary L. Lilien (2008), “Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids,” Marketing Science, 27 (3), 443–60.
Chau, Patrick Y. (2015), “An Empirical Assessment of a Modified Technology Acceptance Model,” Journal of Management Information Systems, 13 (2), 185–204.
Childers, Terry L., Christopher L. Carr, Joann Peck, and Stephen Carson (2001), “Hedonic and utilitarian motivations for online retail shopping behavior,” Journal of Retailing, 77 (4), 511–35.
Davis, Fred D. (1989), “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, 13 (3), 319.
———, Richard P. Bagozzi, and Paul R. Warshaw (1992), “Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1,” Journal of Applied Social Psychology, 22 (14), 1111–32.
Day, George S., Allan D. Shocker, and Rajendra K. Srivastava (1979), “Customer-Oriented Approaches to Identifying Product-Markets,” Journal of Marketing, 43 (4), 8.
DeNale, Rebecca and Deanna Weidenhamer (2017), “U.S. Census Bureau News Quarterly Retail e-Commerce Sales: 4th Quarter 2016,” (Accessed May 4, 2017), [available at https://www.census.gov/retail/ecommerce/historic_releases.html]
Diehl, Kristin (2005), “When Two Rights Make a Wrong. Searching Too Much in Ordered Environments,” Journal of Marketing Research, 42 (3), 313–22.
———, Laura J. Kornish, and John G. Lynch (2003), “Smart Agents. When Lower Search Costs for Quality Information Increase Price Sensitivity,” Journal of Consumer Research, 30 (1), 56–71.
Duan, W., B. Gu, and A. WHINSTON (2008), “The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry,” Journal of Retailing, 84 (2), 233–42.
Edwards, W. and B. Fasolo (2001), “Decision technology,” Annual review of psychology, 52, 581–606.
Ellison, G. and D. Fudenberg (1995), “Word-of-Mouth Communication and Social Learning,” The Quarterly Journal of Economics, 110 (1), 93–125.
Greenwood, Shannon, Andrew Perrin and Maeve Duggan (2016), “Social Media Update 2016,” (Accessed May 15, 2017), [available at http://www.pewinternet.org/2016/11/11/social-media-update-2016/]
Gilbride, Timothy J. and Greg M. Allenby (2004), “A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules,” Marketing Science, 23 (3), 391–406.
Goodhue, Dale L. and Ronald L. Thompson (1995), “Task-Technology Fit and Individual Performance,” MIS Quarterly, 19 (2), 213.
Häubl, Gerald and Valerie Trifts (2000), “Consumer Decision Making in Online Shopping Environments. The Effects of Interactive Decision Aids,” Marketing Science, 19 (1), 4–21.
Hoch, Stephen J. and David A. Schkade (1996), “A Psychological Approach to Decision Support Systems,” Management Science, 42 (1), 51–64.
Hoffman, Donna L. and Thomas P. Novak (1996), “Marketing in Hypermedia Computer-Mediated Environments. Conceptual Foundations,” Journal of Marketing, 60 (3), 50.
Jacoby, Jacob, Donald E. Speller, and Carol A. Kohn (1974), “Brand Choice Behavior as a Function of Information Load,” Journal of Marketing Research, 11 (1), 63.
Johnson, Eric J., Wendy W. Moe, Peter S. Fader, Steven Bellman, and Gerald L. Lohse (2004), “On the Depth and Dynamics of Online Search Behavior,” Management Science, 50 (3), 299–308.
Kasper, George M. (1996), “A Theory of Decision Support System Design for User Calibration,” Information Systems Research, 7 (2), 215–32.
Kim, Jiyeon and Sandra Forsythe (2008), “Adoption of Virtual Try-on technology for online apparel shopping,” Journal of Interactive Marketing, 22 (2), 45–59.
Kollat, David T., James F. Engel, and Roger D. Blackwell (1970), “Current Problems in Consumer Behavior Research,” Journal of Marketing Research, 7 (3), 327.
Komiak, Sherrie Y. X. and Izak Benbasat (2006), “The Effects of Personalizaion and Familiarity on Trust and Adoption of Recommendation Agents,” MIS Q, 30 (4), 941–60.
Koufaris, Marios (2002), “Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior,” Information Systems Research, 13 (2), 205–23.
Laroche, Michel, Nicolas Papadopoulos, Louise A. Heslop, and Mehdi Mourali (2005), “The influence of country image structure on consumer evaluations of foreign products,” International Marketing Review, 22 (1), 96–115.
Lurie, Nicholas H. and Na Wen (2014), “Simple Decision Aids and Consumer Decision Making,” Journal of Retailing, 90 (4), 511–23.
Malhotra, Naresh K. (1982), “Multi-stage information processing behavior. An experimental investigation,” Journal of the Academy of Marketing Science, 10 (1-2), 54–71.
Markus, Hazel R. and Barry Schwartz (2010), “Does Choice Mean Freedom and Well-Being?,” Journal of Consumer Research, 37 (2), 344–55.
Murray, Kyle B. and Gerald Häubl (2009), “Personalization without Interrogation. Towards more Effective Interactions between Consumers and Feature-Based Recommendation Agents,” Journal of Interactive Marketing, 23 (2), 138–46.
Olson, Erik L. and Robert E. Widing (2002), “Are interactive decision aids better than passive decision aids? A comparison with implications for information providers on the internet,” Journal of Interactive Marketing, 16 (2), 22–33.
Paul A. Pavlou (2003), “Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model,” International Journal of Electronic Commerce, 7 (3), 101–34.
Payne, John W. (1976), “Task complexity and contingent processing in decision making. An information search and protocol analysis,” Organizational Behavior and Human Performance, 16 (2), 366–87.
Reibstein, David J., Stuart A. Youngblood, and Howard L. Fromkin (1975), “Number of choices and perceived decision freedom as a determinant of satisfaction and consumer behavior,” Journal of Applied Psychology, 60 (4), 434–37.
Reynolds, Kristy E. and Sharon E. Beatty (1999), “Customer benefits and company consequences of customer-salesperson relationships in retailing,” Journal of Retailing, 75 (1), 11–32.
Rogers, Everett M. (op. 1995), “Diffusion of Innovations: Modifications of a Model for Telecommunications,” in Die Diffusion von Innovationen in der Telekommunikation. Schriftenreihe des wissenschaftlichen Instituts für Kommunikationsdienste, Vol. 17, Matthias-Wolfgang Stoetzer, ed. Berlin: Springer, 25–38.
Shugan, Steven M. (1980), “The Cost of Thinking,” Journal of Consumer Research, 7 (2), 99.
Silverman, Barry G., Mintu Bachann, and Khaled Al-Akharas (2001), “Implications of buyer decision theory for design of e-commerce websites,” International Journal of Human-Computer Studies, 55 (5), 815–44.
Simon, Herbert A. (1955), “A Behavioral Model of Rational Choice,” The Quarterly Journal of Economics, 69 (1), 99.
Song, Jaeki, Donald Jones, and Naveen Gudigantala (2007), “The effects of incorporating compensatory choice strategies in Web-based consumer decision support systems,” Decision Support Systems, 43 (2), 359–74.
Sposito, V. A., M. L. Hand, and Bradley Skarpness (2007), “On the efficiency of using the sample kurtosis in selecting optimal l p estimators,” Communications in Statistics - Simulation and Computation, 12 (3), 265–72.
Stoetzer, Matthias-Wolfgang, ed. (op. 1995), Die Diffusion von Innovationen in der Telekommunikation. Schriftenreihe des wissenschaftlichen Instituts für Kommunikationsdienste, Vol. 17. Berlin: Springer.
Swaminathan, Vanitha (2003), “The Impact of Recommendation Agents on Consumer Evaluation and Choice: The Moderating Role of Category Risk, Product Complexity, and Consumer Knowledge,” Journal of Consumer Psychology, 13 (1-2), 93–101.
Tan, Chuan-Hoo, Hock-Hai Teo, and Izak Benbasat (2010), “Assessing Screening and Evaluation Decision Support Systems. A Resource-Matching Approach,” Information Systems Research, 21 (2), 305–26.
Teo, Thompson S. and Yon D. Yeong (2003), “Assessing the consumer decision process in the digital marketplace,” Omega, 31 (5), 349–63.
Todd, Peter and Izak Benbasat (1999), “Evaluating the Impact of DSS, Cognitive Effort, and Incentives on Strategy Selection,” Information Systems Research, 10 (4), 356–74.
——— and ——— (2000), “Inducing compensatory information processing through decision aids that facilitate effort reduction. An experimental assessment,” Journal of Behavioral Decision Making, 13 (1), 91–106.
van der Heijden, Hans (2004), “User Acceptance of Hedonic Information Systems,” MIS Quarterly, 28 (4), 695–704.
van Zee, Emily H., Thaddeus F. Paluchowski, and Lee R. Beach (1992), “The effects of screening and task partitioning upon evaluations of decision options,” Journal of Behavioral Decision Making, 5 (1), 1–19.
Venkatraman, Meera P. and Linda L. Price (1990), “Differentiating between cognitive and sensory innovativeness,” Journal of Business Research, 20 (4), 293–315.
Xiao, Bo and Izak Benbasat (2007), “E-commerce product recommendation agents: use, characteristics, and impact,” MIS Quarterly, 31 (1), 137–209.
Zhang, Jing and En Mao (2016), “From Online Motivations to Ad Clicks and to Behavioral Intentions. An Empirical Study of Consumer Response to Social Media Advertising,” Psychology & Marketing, 33 (3), 155–64.
Zhang, Ping (2013), Toward a Positive Design Theory: Principles for Designing Motivating Information and Communication Technology.		zh_TW