Influence Processes for Information Technology Acceptance: An Elaboration Likelihood Model

Abstract

This study examines how processes of external influence shape information technology acceptance among potential users, how such influence effects vary across a user population, and whether these effects are persistent over time. Drawing on the elaboration-likelihood model (ELM), we compared two alternative influence processes, the central and peripheral routes, in motivating IT acceptance. These processes were respectively operationalized using the argument quality and source credibility constructs, and linked to perceived usefulness and attitude, the core perceptual drivers of IT acceptance. We further examined how these influence pro-1 Bernard C. Y. Tan was the accepting senior editor for this paper. Sue Brown was the associate editor. Stephanie Watts Sussman, Ronald Thompson, and Duane Wegener served as reviewers. cesses were moderated by users' IT expertise and perceived job relevance and the temporal stability of such influence effects. Nine hypotheses thus developed were empirically validated using a field survey of document management system acceptance at an eastern European governmental agency. This study contributes to the IT acceptance literature by introducing ELM as a referent theory for acceptance research, by elaborating alternative modes of influence, and by specifying factors moderating their effects. For practitioners, this study introduces influence processes as policy tools that managers can employ to motivate IT acceptance within their organizations, benchmarks alternative influence strategies, and demonstrates the need for customizing influence strategies to the specific needs of a user population.

Factors for the Acceptance of Enterprise Resource Planning (ERP) Systems and Financial Performance

“…This particular element of the system is also necessary as it would allow the different actors in the organization to work in unison, leading to significant improvements in the effectiveness of the organization. This particular view of TAM is also necessary as it can lead to many different ways in which the employees can react, and therefore the different aspects need to be taken into account [39], [40].…”

“…Research suggests that the benefits of a satisfied user is highly significant for the adoption of the system, as the user is able to take part in the implementation of the system and the resistance to change from the user is kept to a minimum. Another critical component of the user training during the implementation of a system is that the training environment which the management provides is also part of a collective leaning process [39], [60].…”

Technology Adoption by Elderly People – An Empirical Analysis of Adopters and Non-Adopters of Social Networking Sites

“…A recent publication within MISQ identified perceived values, switching costs or support as factors which can tip the balance and lead to non-adoption [45]. Such factors differ depending on the underlying context, so that other authors identified loss of status or power, uncertainty [40], pressure, exchange [29] or perceived threat ( [7]; [9]) as significant influence factors which increase the probability to reject technologies. Eckhardt et al [27] focused on the other side of social influence and investigated what groups exert an influence on the decision of people to refuse adopting a technology.…”

Automated Promotion of Technology Acceptance by Clinicians Using Relational Agents

“…The TAM has received empirical support across a number of industries and technologies, typically accounting for 30% to 40% of IT acceptance. Very few studies of the TAM have focused on approaches for promoting acceptance using external influences (such as social influences or persuasive messages), partly due to the wide variety of messages that could be employed, the differential impact these messages have on different user groups, and the lack of temporal models explaining the time course of influences [4]. One framework that has been used widely in studies of attitude change and persuasion is the elaboration likelihood model, in which attitude change is hypothesized to be effected through either "central route" cues (argument quality, appealing to reason) or "peripheral route" cues (meta-information about a message or source, such as the recipient's trust in the source) [5].…”

“…One framework that has been used widely in studies of attitude change and persuasion is the elaboration likelihood model, in which attitude change is hypothesized to be effected through either "central route" cues (argument quality, appealing to reason) or "peripheral route" cues (meta-information about a message or source, such as the recipient's trust in the source) [5]. This model has been used in studies of persuasion to improve technology acceptance, demonstrating that central route messages can significantly influence perceived usefulness and peripheral route cues can significantly influence both perceived usefulness and attitude towards the technology [4].…”