Acceptance of Technology with Network Externalities: An Empirical Study of Internet Instant Messaging Services

ABSTRACT Many researchers have examined the technology acceptance model (TAM) that Davis (1986) created to predict the voluntary use of information systems. However, TAM's primary focus is on how ease of use and usefulness influence acceptance, without looking at the effects of network externalities. In this study, we examined adoption behavior involving Internet-based instant messaging services (IMS). A questionnaire was used to collect data on perceived usefulness, perceived ease of use, perceived number of users for external network utility, and technology utility. The results indicate an acceptable goodness-of-fit statistic for our proposed TAM model, which combines the original TAM concept and network externality theory. The results also support the importance of network externalities in considering IT acceptance. INTRODUCTION The success of any information systems development depends on a combination of user acceptance and advancements in technology. Davis's (1986, 1989) technology acceptance model (TAM) is one of the best-known approaches to explaining and predicting user acceptance of information systems. According to Davis, perceived usefulness (PU) and perceived ease of use (PEOU) are the two most important factors determining system usage. He defined PU as the degree to which a person believes that using a particular information system will enhance his or her job performance (Davis 1989); PEOU was defined as "the degree to which a person believes that using a particular information system would be free of effort." The TAM approach assumes that PU is influenced by PEOU - that is, systems that are perceived as easier to use are also perceived as being more useful (Venkatesh & Davis 2000). A long list of researchers have used various technologies to test, assess, and verify TAM, which included Adams, Nelson and Todd (1992), Agarwal and Prasad (1997), Bhattacherjee (2000), Chau (1996), Chau and Hu (2001), Chen and Lou (2002), Chin and Todd (1995), Davis (1989), Davis, Bagozzi, and Warshaw (1989), Doll, Hendrickson and Deng (1998), Gefen and Straub (1997), Hong. Thong, Wong and Tarn (2001), Hu and Chau (1999), Lucas and Spider (1999), Mathieson (1991), Moon and Kim (2001), Szajna (1994), Venkatesh and Davis (2000), Venkatesh, Speier, and Morris (2002), and Wober and Gretzel (2000) and some others. The technologies used to test the effectiveness of TAM include email, voice mail, word processors, spreadsheets, database programs, graphic systems, decision support systems, and the World-Wide Web browsers. In the majority of these studies, use of the TAM model was supported by the empirical results. However, there are other factors that influence behavioral intention to use (BI) and actual usage behavior (AB)-for instance, perceived utility from a network externality effect. According to Rohlfs (1974) and Katz and Shapiro (1985), network externality is the characteristic of change in product value according to the number of users. In short, the utility that a user derives from consuming a good or product increases as the number of consumers of the same good or product also increases (Katz & Shapiro 1986). Communication products serve as one example of merchandise for which network externality strongly influences user utility. Internet-based Instant Messaging Services (IMSs) are relatively recent communication products that possess network externality. User acceptance of an IMS may be determined not only by perceived usefulness or ease of use, but also by the network effect based on the number of users. According to marketing research conducted by Jupiter Media Metrix, 53.8 million Americans used an instant messaging product in their home at least once during September, 2001, an increase of 28 percent compared to September, 2000. A smaller but still significant number of Americans (13.4 million) used instant messaging at work during September of 2001 (Denison, 2002). The adoption behavior of IMS users is the primary focus of this study. …

[1]  Peter A. Todd,et al.  Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication , 1992, MIS Q..

[2]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[3]  L. G. Tornatzky,et al.  Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings , 1982, IEEE Transactions on Engineering Management.

[4]  Richard G. Lomax,et al.  A Beginner's Guide to Structural Equation Modeling , 2022 .

[5]  Detmar W. Straub,et al.  Structural Equation Modeling and Regression: Guidelines for Research Practice , 2000, Commun. Assoc. Inf. Syst..

[6]  Kieran Mathieson,et al.  Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior , 1991, Inf. Syst. Res..

[7]  Mark J. Safferstone Information Rules: A Strategic Guide to the Network Economy , 1999 .

[8]  Ritu Agarwal,et al.  The Role of Innovation Characteristics and Perceived Voluntariness in the Acceptance of Information Technologies , 1997 .

[9]  Ulrike Gretzel,et al.  Tourism Managers’ Adoption of Marketing Decision Support Systems , 2000 .

[10]  Aleda V. Roth,et al.  Enterprise Resource Planning (ERP) Competence Constructs: Two-Stage Multi-Item Scale Development and Validation , 2002, Decis. Sci..

[11]  Robert J. Kauffman,et al.  Opening the "Black Box" of Network Externalities in Network Adoption , 2000, Inf. Syst. Res..

[12]  Sujoy Chakravarty,et al.  Experimental Evidence on Product Adoption in the Presence of Network Externalities , 2003 .

[13]  P. Bentler On the fit of models to covariances and methodology to the Bulletin. , 1992, Psychological bulletin.

[14]  Joseph Farrell,et al.  Installed base and compatibility : innovation, product preannouncements and predation , 1986 .

[15]  Albert H. Segars,et al.  Strategic Information Systems Planning Success: An Investigation of the Construct and Its Measurement , 1998, MIS Q..

[16]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[17]  C. Shapiro,et al.  Network Externalities, Competition, and Compatibility , 1985 .

[18]  D. Allen New telecommunications services: Network externalities and critical mass☆ , 1988 .

[19]  Lyman E. Ostlund Perceived Innovation Attributes as Predictors of Innovativeness , 1974 .

[20]  Detmar W. Straub,et al.  Validation in Information Systems Research: A State-of-the-Art Assessment , 2001, MIS Q..

[21]  Richard P. Bagozzi,et al.  Assessing Construct Validity in Organizational Research , 1991 .

[22]  Bernadette Szajna,et al.  Software Evaluation and Choice: Predictive Validation of the Technology Acceptance Instrument , 1994, MIS Q..

[23]  Garth Saloner,et al.  Digitized by the Internet Archive in 2011 with Funding from Adoption of Technologies Uith Network Effects: an Empirical Examination of the Adoption of Automated Teller Machines , 2022 .

[24]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[25]  Wynne W. Chin,et al.  On the use, usefulness, and ease of use of structural equation modeling in MIS research: a note of caution , 1995 .

[26]  Olivia R. Liu Sheng,et al.  Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology , 1999, J. Manag. Inf. Syst..

[27]  Gary Klein,et al.  Measuring Information System Service Quality: SERVQUAL from the Other Side , 2002, MIS Q..

[28]  Henry C. Lucas,et al.  Technology Use and Performance: A Field Study of Broker Workstations* , 1999 .

[29]  Detmar W. Straub,et al.  Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model , 1997, MIS Q..

[30]  Michael L. Katz,et al.  Product Compatibility Choice in a Market with Technological Progress , 1986 .

[31]  Detmar W. Straub,et al.  Measuring System Usage: Implications for IS Theory Testing , 1995 .

[32]  B. Byrne Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts, Applications, and Programming , 1998 .

[33]  Fred D. Davis A technology acceptance model for empirically testing new end-user information systems : theory and results , 1985 .

[34]  Diane M. Strong,et al.  Perceived critical mass effect on groupware acceptance , 2000, Eur. J. Inf. Syst..

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

[36]  Detmar W. Straub,et al.  Trust and TAM in Online Shopping: An Integrated Model , 2003, MIS Q..

[37]  Patrick Y. K. Chau,et al.  An Empirical Assessment of a Modified Technology Acceptance Model , 1996, J. Manag. Inf. Syst..

[38]  Viswanath Venkatesh,et al.  User Acceptance Enablers in Individual Decision Making About Technology: Toward an Integrated Model , 2002, Decis. Sci..

[39]  Anthony R. Hendrickson,et al.  Using Davis's Perceived Usefulness and Ease-of-use Instruments for Decision Making: A Confirmatory and Multigroup Invariance Analysis , 1998 .

[40]  Bernadette Szajna,et al.  Empirical evaluation of the revised technology acceptance model , 1996 .

[41]  R. Bagozzi An Examination Of The Validity Of Two Models Of Attitude. , 1981, Multivariate behavioral research.

[42]  Alfred Clinton,et al.  Attributes of Innovations as Factors in Diffusion. , 1970 .

[43]  Rense Lange,et al.  The Estimation of a Cusp Model to Describe the Adoption of Word for Windows , 2004 .

[44]  Yining Chen,et al.  Toward an Understanding of the Behavioral Intention to Use a Groupware Application , 2000, J. Organ. End User Comput..

[45]  C. Wisner A STRUCTURAL EQUATION MODEL OF SUPPLY CHAIN MANAGEMENT STRATEGIES AND FIRM PERFORMANCE , 2003 .

[46]  Paul Jen-Hwa Hu,et al.  Information Technology Acceptance by Individual Professionals: A Model Comparison Approach , 2001, Decis. Sci..

[47]  Jeffrey H. Rohlfs A theory of interdependent demand for a communications service , 1974 .

[48]  Anol Bhattacherjee,et al.  Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..

[49]  Amy B. Woszczynski,et al.  The Problem of Common Method Variance in IS Research , 2004 .

[50]  John Hattie,et al.  The relationship between marital characteristics, marital interaction processes, and marital satisfaction , 2004 .

[51]  Young-Gul Kim,et al.  Extending the TAM for a World-Wide-Web context , 2000, Inf. Manag..

[52]  Kar Yan Tam,et al.  Determinants of User Acceptance of Digital Libraries: An Empirical Examination of Individual Differences and System Characteristics , 2002, J. Manag. Inf. Syst..

[53]  P. M. Podsakoff,et al.  Self-Reports in Organizational Research: Problems and Prospects , 1986 .

[54]  Sanjay L. Ahire,et al.  Development and Validation of TQM Implementation Constructs , 1996 .