A Theoretical extension of the technology acceptance model to explain the

A Theoretical extension of the technology acceptance model to explain the adoption and the usage of new digital services Jean Philippe Galan Professeur des Universités CRM (UMR 5303 CNRS/UT1) IAE Université de Valenciennes et du Hainaut Cambrésis jean-philippe.galan@univ-valenciennes.fr / +33(0)5.61.63.56.79 Magali Giraud, Maître de Conférences CRM (UMR 5303 CNRS/UT1) IAE Université de Toulouse I Capitole magali.giraud@iae-toulouse.fr / +33(0)5.61.63.56.79 Lars Meyer-Waarden, Professeur des Universités CRM (UMR 5303 CNRS/UT1) IAE Université de Toulouse I Capitole lars.meyer-waarden@iae-toulouse.fr / +33(0)6.80.37.42.08 0

A Theoretical Extension of the Technology Acceptance Model to explain the adoption and the usage of new digital services Abstract: This research develops a theoretical extension of the Technology Acceptance Model (TAM). We introduce complementary variables: social image, self-efficacy, hedonism, innovativeness, privacy concern, trust. The extended model (TAM2), was tested regarding three new digital services (leisure, pedagogy, administration; N = 2205). Trust plays a key role in the adoption process and has even more impact on intention of use than perceived ease of use and perceived usefulness, whatever the domain of application (leisure, pedagogy or administration). Perceived hedonic benefits enhance perceived easiness and usefulness of usage of the new digital service. Keywords: TAM, Trust, social image, hedonism, privacy, innovativeness. 1

A Theoretical Extension of the Technology Acceptance Model to explain the adoption and the usage of new digital services Introduction New information technology (IT) adoption is a central concern of customer relationship management, e-marketing and e-commerce. A lot of IT systems fail because users do not adopt and use them, either because of the difficulty of use, or because of the user reluctance. Understanding and creating the conditions under which IT systems will be embraced by the human organization therefore remains a high-priority research issue. Substantial theoretical and empirical progress has been made in explaining and predicting user acceptance of IT. In particular, the Technology Acceptance Model (TAM) has become well-established as a model for predicting IT acceptance, usage intentions and behavior via the mediating variables perceived usefulness and perceived ease of use (Davis 1989, Davis, Bagozzi & Warshaw, 1989). Therefore, the first goal of the present research is to extend the TAM by including additional, no investigated key determinants of perceived usefulness and ease of use and to apply it to future digital university campus services. The second target of this research is to highlight moderators of innovation adoption processes. Previous research has shown that innovation acceptation processes depend on system-related factors (e.g. Nysveen, Pedersen & Thorbjornsen, 2005). By investigating three different domains of students digital consumption and IT services (leisure, pedagogy and administrative procedures), this research aims at understanding different moderating effects linked to the field of application of the IT system. This article is structured as follows: after reviewing key concepts about the TAM, we shall 2

explain our conceptual framework and hypotheses. We then describe our methodology and surveys conducted. The results shall be presented and we conclude the article with a discussion, managerial implications and directions of future research. 2. Key concepts, conceptual framework and hypotheses We define, in the following sections, the concepts used in our conceptual framework and then present the hypotheses related to our core issues. 2.1. Basic TAM model Rooted in the Theory of Reasoned Action (Ajzen & Fishbein, 1980), TAM is a framework for predicting and explaining consumers' adoption of IT (Davis, 1989). It is a framework for predicting and explaining consumers' adoption of information technology. It postulates that user acceptance of a new system is determined by the users intention to use (IU) the system, which is influenced by the users beliefs about the system s perceived usefulness (PU) and perceived ease of use (PEU). Both variables are influenced by external variables, such perceived accessibility (Karahanna & Straub, 1999), social influence processes, and cognitive instrumental processes (Venkatesh & Davis, 2000). 2.2. Extended TAM model Figure 2 shows our extended model. Using TAM as the starting point, our model incorporates additional theoretical constructs spanning different aspects of social influence processes, perceived hedonism and cognitive instrumental processes. 3

Perceived Social image (PSI) Perceived Self Congruity (PSC) Perceived Hedonism (PH) Perceived self efficacy (PSE) Innovativeness (INO) Perceived protection private live (PPPL) Perceived usefulness (PU) Perceived ease of use (PEU) Trust (TT) Intention to use (IU) 2.2.1. Social Influence Processes Type of service (leisure, administration, pedagogy Figure 1. Extended TAM model (TAM2) Our model reflects the impact of two social forces impinging innovation adoption. -Perceived social image (PSI) In our research we define social image (PSI) as the degree to which use of an innovation is perceived to enhance one's social status in one's social group (Moore & Benbasat, 1991). A technology will be considered all the more useful as it helps persons to be consistent with a groups norms. They may perceive that using a system will lead to improvements in their performance indirectly due to image enhancement. We hypothesize: H1a: PSI to others by adopting a technology is positively correlated with PU. 4

H1b: PU of a technology is positively correlated with IU. - Self Congruity (PSC) Consumers are motivated to purchase products which are congruent with their beliefs about themselves (Sirgy, 1982). A product perceived as congruent may be considered easier to use and more useful as an incongruent one. H2a : PSC is positively correlated with PEU of a technology X2b : PSC is positively correlated with PU of a technology 2.2.2 Cognitive Instrumental Processes We theorize three cognitive instrumental determinants affecting perceived usefulness and usage intention: perceived ease of use, self-efficacy with the technology and perceived hedonism of technology. - Perceived ease of use (PEU) In accordance with the basic TAM, our extended model retains perceived ease of use from TAM as a direct determinant of perceived utility (PU) and an antecedent of intention to use (IU), both directly and indirectly via its impact on perceived utility (Davis, Bagozzi and Warshaw, 1989). We therefore hypothesize: H3a: PEU of a technology is positively correlated with PU. H3b: PEU of a technology is positively correlated with IU. - Perceived self-efficacy (PSE) 5

Perceived self-efficacy (PSE) is the measure of one's own competence to complete tasks and reach goals in specific situations (Bandura, 1997). In the process of technology adoption, people generally avoid to adopt technologies where their PSE is low, because they overestimate efforts it will require (Venkatesh and Davis, 1996). We hypothesize: H4a: High consumers PSE is positively correlated with PEU of a technology. H4b: High consumers PSE is positively correlated with PU of a technology. - Innovativeness (INO) Innovativeness has been defined as the willingness of an individual to adopt and try out any innovation (Rogers, 1983). Insofar innovative people are opened to new experiences and risk taking, they are less reluctant to adopt a new technology, as they anticipate less risks and efforts, and as they have more positive beliefs about technology use (Agarwal and Karahanna, 2000). Hence, we hypothesize: H5a: Consumers innovativeness is positively correlated with PEU of a technology. H5b: Consumers innovativeness is positively correlated with PU of a technology. - Perceived hedonism (PH) One of the drawbacks of the TAM is that it does not take into account emotions as a predictor of perceived utility toward the act of using the new technology and usage intention. Indeed, consumer behavior theory provides evidences that utilitarian motives (economic and functional) are not sufficient to explain consumer behavior toward a product (Chitturi, Raghunathan and Mahajan, 2008). Hedonic motivation has been shown to influence technology acceptance and use very significantly, sometimes more than PU (Van der Heijden, 6

2004). We therefore hypothesize: H6a: A technology PH is positively correlated with PEU. H6b: A technology PH is positively correlated with PU. - Perceived protection of private live (PPPL) and trust in technology (TT) Most commercial IT systems and associated databases (e.g. Google, Facebook, e- commercents such as Amazon.com) collect personal data associated with individual consumers in intimate ways that can be used to tailor personalised advertisements. This can be seen as intrusion and arouses concerns on privacy (Phelps, D Souza and Nowak, 2001). The adoption of IT then depends heavily on the development of trust between the provider, the consumer and the IT systems. Perceived trust in new IT proves to be a direct antecedent of intention to use (IU) (Sirdeshmukh, Singh and Sabol, 2002; Dimitriadis and Kyrezis, 2010) and mediates the influence of PPPL on IU (Liu & al., 2004). We therefore hypothesize: H7a: PPPL about a technology is positively correlated with their trust in it. H7b: Trust in a technology is positively correlated with intention to use. 2.3 Moderating effects Previous studies highlight some differences in processes underlying innovation adoption depending on the field of application of the technology (e.g. telephone banking vs internet banking - Dimitriadis and Kyrezis, 2010). We therefore hypothesize that the context of use of the innovation is going to moderate relations between the variables of the model. H8: The relations between the variables of TAM2 are moderated by the context of use of the technology. 7

3. Research methodology Our investigation based on scenario experimental methodology has been carried out together with the microprocessor firm Intel to test the TAM2 model for three new digital campus life services that should facilitate administrative, academic/pedagogical and leisure activities. Theoretical constructs were operationalized using validated items from prior research. All the constructs were measured with multi-item Likert scales (1 = strongly disagree to 7 = strongly agree). The TAM scales of perceived usefulness, perceived ease of use, and intention to use were measured using items adapted from Davis (1989) and Davis, Bagozzi and Warshaw (1989). Trust scale items were taken from Dimitriadis and Kyrezis (2010). Perceived selfcongruity items were taken from Sirgy and Su (2000). Innovativeness was measured through five items adapted from Oliver and Bearden (1985) and Goldsmith and Hofacker (1991). Perceived protection of private life was measured through items adapted from Jarvenpaa, Tractinsky and Vitale (2000). Perceived self efficacy toward IT was measured through the scale of Faurie and van de Leemput (2007). We adapted Venkatesh et al. (2012) scales to measure perceived hedonism. Finally, PSI was measured through the scale of Sweeney and Soutar (2001). The surveys were conducted between 2011 and 2012 on a sample of 2205 undergraduate and graduate students attending the university Toulouse (France). A structural equation model (SEM) was employed to test the hypotheses of this research 4. Results During the scales validation process, we had to eliminate one item linked to the PSE scale and one item linked to trust to improve constructs reliability. The other scales did not require any modification. Results are satisfying concerning reliability (Cronbach's alpha and Joreskog ρ over 0,7), convergent validity (ρ vc around or above 0,5) and discriminant validity (ρ vc below 8

γ²). The model presents a good fit, as indicated by indices of goodness of fit (table 1). GFI AGFI CFI RMSEA SRMR AIC 0,960 0,947 0,975 0,037 (p <1) 0,0292 1534 (42303) Table 1. Global fit of measurement model Hypothesis H1 to H8 were tested through a structural equation modelling analysis (figure 2). This model presents a good-fit (cf. table 2). PSI PSC PSE PH INO PPPL PU PEU TT IU Relation significant Relation non significant Figure 2 Structural equation model X²/ddl GFI AGFI CFI RMSEA AIC 7,61 0,937 0,918 0,947 0,054 (p <,07) 1984 (32304) Table 2. Global fit of structural model There is a positive and significant link (β=,111 ; p<,000) between perceived social image and perceived usefulness, supporting hypothesis H1a. The link between perceived usefulness and intention to use is positive and significant (β=,218 ; p<,000), confirming H1b. Perceived selfcongruity exerts an influence significant and positive on perceived usefulness (β=,118 ; p<,000), confirming H2b, and on perceived ease of use (β=,087 ; p<,007), supporting 9

H2a.The links between perceived ease of use and perceived usefulness (β=,331 ; p<,000), as well as perceived ease of use and intention to use (β=,140 ; p<,000) are positive and significant, confirming hypotheses H3a and b. The relation between perceived self-efficacy and perceived ease of use is positive and significant (β=,364 ; p<,000), whereas the relation between perceived self-efficacy and perceived usefulness is non-significant (β=-0,010 ; p<,764), confirming H4a and rejecting H4b. Results indicate that innovativeness influences significantly perceived ease of use (β=,159 ; p<,000) and perceived usefulness (β=,084 ; p<,008), confirming H5a and H5b. As anticipated in H6a and b, perceived hedonism positively and significantly influences perceived ease of use (β=,094 ; p<,000) and perceived usefulness (β=,153 ; p<,000). Perceived protection of private life influences positively and significantly trust (β=,529 ; p<,000) and trust influences positively and significantly intention to use, confirming H7a and b. Pedagogy Administration Leisure Test Relation β stand. β p β stand. β p β stand. β p Δχ² [2] p PSE! PEU,424,343,000,325,220,000,290,230,000 3,789,150 PH! PEU,083,059,051,080,050,055,133,101,003 1,494,474 PSC! PEU,070,048,185,090,066,162,115,090,100,409,815 INO! PEU,165,137,009,155,132,007,248,227,000 2,127,345 PSE! PU,014,011,844,023,017,701 -,037 -,033,470,691,708 PH! PU,230,163,000,057,038,180,307,264,000 24,056,000 PSI! PU,028,020,600,079,062,219,134,107,043 1,856,395 PSC! PU,161,110,004,031,025,631,185,162,005 3,384,184 INO! PU -,027 -,022,682,177,162,002,064,066,227 5,915,052 PEU! PU,384,381,000,310,334,000,235,265,000 2,956,228 10

PPPL! TT,528,503,000,545,608,000,460,452,000 5,789,055 TT! IU,615,569,000,617,513,000,692,655,000 6,781,033 PU! IU,208,226,000,218,216,000,198,193,000,376,828 PEU! IU,243,261,000,157,168,000,090,099,005 9,241,009 PSI: Perc. social image PU: perc. usefulness IU: Intention to use PEU: Perc. ease of use PSE: Perc. self-efficacy PSC: Perc. self-congruity, PH: Perc. hedonism, INO: Innovativeness PPPL: Perc. protection of private live, TT: Trust Table 3. Moderating effect of context We then conducted a multi-group analysis to test the influence of the context or type of the digital services (administration, pedagogy or leisure) on the TAM2. This procedure relies on the analysis of variations of the global fit index χ². First, we verified that measurements do not vary across the three domains, to ensure that differences cannot be attributed to measurement instability. We thus constrained parameters linking constructs and their measurements. We got a difference Δχ² (38) = 47,126 (p<,147) which ensures the absence of measurement differences. Second, we constrained the causal relations between the latent constructs for which we hypothesize the moderation effects and obtain a variation Δχ² (28) 106,32 (p<,000) supporting H8, stipulating the moderating impact of the type of digital services offered. Nevertheless, every relation is not affected by this moderating effect. The moderating effect is significant (p<0,05) on the relation between perceived hedonism and perceived usefulness (Δχ²²(2) = 24,056 ; p<,000), between trust and intention to use (Δχ² (2) = 6,781 ; p<,033) and on perceived ease of use on intention to use (Δχ²(2) = 9,241 ; p<,009). Two relations are only slightly moderated: innovativeness on perceived usefulness (Δχ²(2) = 5,915 ; p<,052) and perceived privacy on trust (Δχ² (2) = 5,789 ; p<,055). 11

First, we note (cf. table 3) that the influence of perceived hedonism on perceived usefulness is significantly lower for administrative digital services than leisure and pedagogy services. Second, trust has a stronger influence on intention to use for digital leisure services, than for administration and pedagogy services. Finally, the relation between perceived ease of use and intention to use is weaker for digital leisure services than for administrative and pedagogical services. 5. Discussion of Results The primary goal of this research was to enrich the TAM. The most important result is the key role played by trust in the IT adoption process. Trust appears to have more impact on intention of use than perceived ease of use and perceived usefulness, whatever the domain of application (leisure, pedagogy or administration). Several additional variables indirectly affect behavioral intentions through perceived ease of use and/or perceived utility. The model enhances the importance of perceived competences and innovativeness in perceived ease of use. Feeling self-efficient make consumers more confident about their ability to use new technologies. Innovators, on their side, derive a positive stimulation from using a new product. Learning may also not be considered as a painful effort. The model also emphasizes a hedonic path to innovation acceptance. Consumers consider an innovation all the more easy to use and useful if they feel that it gives them a hedonic benefit. An innovative IT system or digital service can therefore create value not only through its utilitarian benefits but also through the emotional experience associated with its use (Novak, Hoffman and Yung, 2000). 12

Surprisingly, social influence plays a secondary role for the adoption of all digital services. This confirms that the role of social influence on innovation adoption remains quite ambiguous (Lewis, Agarwal and Sambamurthy, 2003; Scheppers and Wetzel, 2007). The second important finding is that psychological processes underlying innovation acceptation were moderated by the nature of the services (leisure, studies, and administrative services). Ease of use only slightly affects intention to use when the digital service is dedicated to leisure, whereas its influence is very significant for the two utilitarian types of service (administration and pedagogy). When hedonism is perceived, the influence of ease of use on intention of use thus decrease. There is also a positive influence of perceived hedonism on perceived usefulness. When the IT system provides hedonic value, it therefore seems that time and efforts associated to use are perceived as less costly for consumers as consumers derive more hedonist values. Whatever the domain of application (leisure,...), users are aware of the potential risks of the IT use for their private life (intrusion). Even hedonic benefits cannot compensate a lack of perceived protection of private life. 6. Managerial Implications The most significant managerial implication of this research is that lack of trust and privacy concerns remain the main obstacle to a widespread adoption of IT systems. Security must be the central topic both in IT development and communication. Results regarding innovation and self-confidence have implications for communication toward experts and opinion leaders who are innovators and/or self-confident. As they experience fewer difficulties in the use of an innovative system, managers should rely on them to convince consumers that technologies are easy to use. Finally, the marketers of IT 13

systems (such as e-learning) must take into account that consumers' expectancies are not strictly utilitarian: information IT systems will be all the more accepted if they are entertaining and safe for their privacy. 7. Limitations and Directions of Research This empirical research has some limitations and leaves many questions unanswered. From a theoretical point of view, it only examines antecedents of technology acceptance. It would be interesting to examine the impact of the variables on the "real" use of a IT system or a digital service, with behavioral loyalty and usage indicators. Theory on technology adoption suggests several direct links between the model variables that have not been tested in this research (e.g. influence of perceived ease of use on perceived hedonism Van der Heidjen, 2004; influence of perceived ease of use on trust intentions Gefen, Karahanna and Straub, 2003). The methodology of the research may also induce some biases. IT systems and digital services were described through scenarios to respondents but not directly experienced. Ease of use was therefore difficult to assess. In a next step, we will propose students to test real prototypes which should provide a more reliable measures and results. References Agarwal R. and Karahanna E. (2000), Time Flies When You're Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage, MIS Quaterly, 24, 4, 665-694. Ajzen I. & Fishbein, M (1980), Understanding attitudes and predicting social behavior, Englewood Cliffs, NJ: Prentice-Hall Bandura A. (1997), Self-efficacy: The exercise of control, New York: Freeman. 14

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Appendix Reliability and construct validity Scale α ρ ρ vc PSI Perceived Social Image (Sweeney and Soutar, 2001),930,923,800 PEU Perceived Ease of Use (Davis, 1989 ; Davis and al. 1989),879,897,743 PPPL Perceived Protection of Private Life (Jarvenpaa and al., 2000),848,871,692 INO - Innovativeness (Godsmith and Hofacker, 1991 ; Oliver and Bearden, 1985),807,812,591 PSE Perceived Self Efficacy (Faurie and vand de Leemput, 2007),786,741,490 TT - Trust (Dimitriadis and Kyrezis, 2010),891,904,824 IU Intention to Use (Davis, 1989 ; Davis and al. 1989),900,919,792 PU Perceived Usefulness (Davis, 1989 ; Davis and al. 1989),752,855,663 PH Perceived Hedonism (Venkatesh and al. 2012),859,890,802 PSC Perceived Self-congruity (Sirgy and Su, 2000),940,946,815 Table 1. Reliability and convergent validity PH,802 PH PSI PEU PSE INO PPPL PU TT IU PSC Auteur Supprimé: PSI,127,800 PEU,104,050,663 PSE,034,019,062,490 INO,087,019,089,369,591 PPPL,009,058,072,077,067,692 PU,034,004,190,206,168,044,743 TT,049,029,338,177,195,240,286,824 IU,071,051,303,184,225,192,240,578,792 PSC,129,550,018,059,070,074,092,074,111,815 Table 2. Discriminant validity Auteur Mis en forme: Police :Gras 19