Influences of gender and product type on online purchasing

This study examines gender differences in the online purchasing behavior of consumers who purchase digital and non-digital goods. The research model builds upon the extended unified theory of acceptance and use of technology (UTAUT2), adding two key e-commerce variables: perceived risk and trust. Empirical analysis uses data from 817 Spanish consumers' responses to an online questionnaire. Gender differences—not considering product type effect—are significant in relationships between effort expectancy and purchase intention and between social influence and purchase intention. Product type affects the relationship between perceived risk and purchase intention in digital goods, where the influence is significantly higher for women. Significant gender differences don't appear for purchase intention in non-digital goods. Product type significantly influences the relationship between performance expectancy and purchase intention, and between facilitating conditions and purchase intention. Product type significantly influences the relationship between perceived risk and purchase intention for women but not for men.

[1]  M. Laroche,et al.  A Three-Dimensional Scale of Intangibility , 2001 .

[2]  Lingyun Qiu,et al.  Applying TAM in B2C E-commerce research: An extended model , 2008 .

[3]  Rob Law,et al.  A comparison of competing theoretical models for understanding acceptance behavior of information systems in upscale hotels , 2009 .

[4]  Bart J. Bronnenberg,et al.  Exploring the implications of the internet for consumer marketing , 1997 .

[5]  Qiang Ye,et al.  A service-oriented analysis of online product classification methods , 2011, Decis. Support Syst..

[6]  Moez Limayem,et al.  How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance , 2007, MIS Q..

[7]  Rudolf R. Sinkovics,et al.  The Use of Partial Least Squares Path Modeling in International Marketing , 2009 .

[8]  Fred D. Davis,et al.  Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1 , 1992 .

[9]  H. Belloc.  The Free Press , 2002 .

[10]  Viswanath Venkatesh,et al.  Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..

[11]  Yujong Hwang,et al.  The moderating effects of gender on e-commerce systems adoption factors: An empirical investigation , 2010, Comput. Hum. Behav..

[12]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[13]  H. Dittmar,et al.  Buying on the Internet: Gender Differences in On-line and Conventional Buying Motivations , 2004 .

[14]  Jiunn-Woei Lian,et al.  Effects of consumer characteristics on their acceptance of online shopping: Comparisons among different product types , 2008, Comput. Hum. Behav..

[15]  Susie Chang.  Internet Segmentation: State-of-the-Art Marketing Applications , 1998 .

[16]  Wynne W. Chin,et al.  An Introduction to a Permutation Based Procedure for Multi-Group PLS Analysis: Results of Tests of Differences on Simulated Data and a Cross Cultural Analysis of the Sourcing of Information System Services Between Germany and the USA , 2010 .

[17]  Xiao Liu,et al.  An empirical study of product differences in consumers' E-commerce adoption behavior , 2003, Electron. Commer. Res. Appl..

[18]  Shelly Rodgers,et al.  Gender and e-commerce: an exploratory study , 2003, Journal of Advertising Research.

[19]  M. Laroche,et al.  Internet versus Bricks-and-Mortar Retailers: An Investigation into Intangibility and Its Consequences , 2005 .

[20]  Leo R. Vijayasarathy,et al.  Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model , 2004, Inf. Manag..

[21]  Jörg Henseler,et al.  Testing Moderating Effects in PLS Path Models. An Illustration of Available Procedures , 2005 .

[22]  D. Gefen,et al.  E-commerce: the role of familiarity and trust , 2000 .

[23]  L. Stoel,et al.  Consumer e-shopping acceptance: Antecedents in a technology acceptance model , 2009 .

[24]  Ellen Garbarino,et al.  GENDER DIFFERENCES IN THE PERCEIVED RISK OF BUYING ONLINE AND THE EFFECTS OF RECEIVING A SITE RECOMMENDATION , 2004 .

[25]  Peter A. Todd,et al.  Assessing IT usage: the role of prior experience , 1995 .

[26]  Thompson S. H. Teo,et al.  Consumer trust in e-commerce in the United States, Singapore and China , 2007 .

[27]  Rajiv Kishore,et al.  An extension of the UTAUT model with e-quality, trust, and satisfaction constructs , 2006, SIGMIS CPR '06.

[28]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[29]  Gurpreet Dhillon,et al.  Interpreting Dimensions of Consumer Trust in E-Commerce , 2003, Inf. Technol. Manag..

[30]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[31]  Lazaros Sarigiannidis,et al.  Consumer characteristics and their effect on accepting online shopping, in the context of different product types , 2016 .

[32]  I. Ajzen.  The theory of planned behavior , 1991 .

[33]  Claire Payne,et al.  Engendering trust in e‐commerce: a study of gender‐based concerns , 2002 .

[34]  Kwangtae Park,et al.  Empirical taxonomy of services and service products in electronic commerce , 2002, Electron. Commer. Res. Appl..

[35]  P. Kotler,et al.  Principles of Marketing , 1983 .

[36]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[37]  Hsin Hsin Chang,et al.  Task-technology fit and user acceptance of online auction , 2010, Int. J. Hum. Comput. Stud..

[38]  Bassam Hasan,et al.  Exploring gender differences in online shopping attitude , 2010, Comput. Hum. Behav..

[39]  Jaishankar Ganesh,et al.  Online Shopper Motivations, and e-Store Attributes: An Examination of Online Patronage Behavior and Shopper Typologies , 2010 .

[40]  Christy M. K. Cheung,et al.  How Habit Limits the Predictive Power of Intentions: The Case of IS Continuance , 2007 .

[41]  Kevin P. Hwang,et al.  The combined model of influencing on-line consumer behavior , 2010, Expert Syst. Appl..

[42]  E. Rogers.  Diffusion of Innovations , 1962 .

[43]  Marko Sarstedt,et al.  Multigroup Analysis in Partial Least Squares (PLS) Path Modeling: Alternative Methods and Empirical Results , 2011 .

[44]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[45]  Chun-Heng Ho,et al.  Integrated Unified Theory of Acceptance and Use of Technology model to explore the female factors with the intention of online shopping, online shopping behavior , 2012, 2012 8th International Conference on Computing and Networking Technology (INC, ICCIS and ICMIC).

[46]  C. Yu.  Factors Affecting Individuals to Adopt Mobile Banking: Empirical Evidence from the Utaut Model , 2012 .

[47]  France Bélanger,et al.  Gender differences in perceptions of web-based shopping , 2002, CACM.

[48]  Paul A. Pavlou,et al.  Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model , 2003, Int. J. Electron. Commer..

[49]  P. Dion,et al.  Validating the search, experience, and credence product classification framework , 2010 .

[50]  Loren V. Geistfeld,et al.  A cross-cultural investigation of consumer e-shopping adoption , 2004 .

[51]  J. Steenkamp,et al.  International market segmentation: issues and perspectives , 2002 .

[52]  Cristian Morosan,et al.  Users’ perceptions of two types of hotel reservation Web sites , 2008 .