Individual and network influences on the adoption and perceived outcomes of electronic messaging

Rice, Ronald E.; Grant, August E.; Schmitz, Joseph; Torobin, Jack

doi:10.1016/0378-8733(90)90021-z

Cited by 250 publications

(106 citation statements)

References 34 publications

Supporting

Mentioning

100

Contrasting

Unclassified

Order By: Relevance

“…These theories converge regarding the central role of the process by which people are influenced in communications and interactions with others, which leads to the creation of social influence (Leenders 2002). By communicating and interacting with one another, people create social influences that affect their opinions, attitudes, and behaviors (Rice et al 1990, Leenders 2002, Iyengar et al 2011). Bruyn and Lilien (2008) report that personal interactions among acquaintances impact not only consumption choice and purchase decision but also expectation, pre-usage attitude, and post-usage perception.…”

Section: Theoretical Foundationsmentioning

confidence: 99%

“…Specifically, social influences, or the impacts created through the interactions among people in a social context (Rice et al 1990), represent an important force affecting individuals' adoption behaviors in a social network (Ibarra and Andrews 1993, Leenders 2002, Bruyn and Lilien 2008, Shalizi and Thomas 2011. Social comparison theory (Festinger 1950) also suggests that people are motivated to evaluate their opinions and behaviors by comparing themselves with others.…”

Section: Theoretical Foundationsmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Adoption Probabilities in Social Networks

Fang

et al. 2013

Information Systems Research

112

View full text Add to dashboard Cite

In a social network, adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Such probabilities are central to fundamental issues in social network analysis, including the influence maximization problem. In practice, adoption probabilities have significant implications for applications ranging from social network-based target marketing to political campaigns; yet, predicting adoption probabilities has not received sufficient research attention. Building on relevant social network theories, we identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors. We then develop the locally-weighted expectation-maximization method for Naïve Bayesian learning to predict adoption probabilities on the basis of these factors. The principal challenge addressed in this study is how to predict adoption probabilities in the presence of confounding factors that are generally unobserved. Using data from two large-scale social networks, we demonstrate the effectiveness of the proposed method. The empirical results also suggest that cascade methods primarily using social influence to predict adoption probabilities offer limited predictive power, and that confounding factors are critical to adoption probability predictions.

show abstract

Section: Theoretical Foundationsmentioning

confidence: 99%

Section: Theoretical Foundationsmentioning

confidence: 99%

Predicting Adoption Probabilities in Social Networks

Fang

et al. 2013

Information Systems Research

112

View full text Add to dashboard Cite

show abstract

“…Individual relations, dynamic relations, and group relations are three operatives of social influence. Students' evaluations of their communicative networks, similar adoption behaviours (Rice, Grant, Schmitz & Torobin, 1990), and group memberships (Festinger, 1954) will affect their attitude and behaviours toward new technology. However, the low interaction between learning groups may reduce group membership among group members, in turn reducing the effect of students' perceived social influence of others while actually using the Wiki system for individual and collaborative learning.…”

Section: Factors Affecting the Intention Of Using Wiki For Collaboratmentioning

confidence: 99%

Exploring factors affecting students’ continued Wiki use for individual and collaborative learning: An extended UTAUT perspective

Yueh

Huang

Chang

2015

AJET

View full text Add to dashboard Cite

The purpose of this study was to investigate what factors affect students' adaptation and continued use of a Wiki system for collaborative writing tasks through an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT). This study was conducted in a general education course in a university in northern Taiwan. Data were collected from surveys of 103 students after their continued writing on the CourseWiki system for one semester. With structural equation model (SEM) analysis, results from this study partially confirm that, in the UTAUT model, factors of social influence have direct and significant effects on students' actual usage of the Wiki system. Moreover, students' actual use of the Wiki influences their intention of future use. This study successfully applies the extended UTAUT model and tests the predictive power in determining students' acceptance of a Wiki system in terms of their actual use and their behavioural intentions of future use. It further provides suggestions for future research and practice, and should contribute to theoretical understanding as well as practical application of learning technology in post-secondary teaching.

show abstract

“…Second, although Fulk et al (1990) argue that social influences are vital predictors of media choice, they lack in operationalization of the concepts and neglect other contextual influences such as organization size. Further, apart from the poor operationalization and the notion that empirical evidence for the SIM is delivered in parts, various others validated the core notion of SIM (e.g., Rice, Grant, Schmitz & Torobin, 1990;Carlson & Zmud, 1994;Kraut, Rice, Cool & Fish, 1998).…”

Section: Constructed Choicementioning

confidence: 99%

What's your favorite blend? : Analyzing source and channel choices in business-to-government service interactions

Boer¹

View full text Add to dashboard Cite

Individual and network influences on the adoption and perceived outcomes of electronic messaging

Cited by 250 publications

References 34 publications

Predicting Adoption Probabilities in Social Networks

Predicting Adoption Probabilities in Social Networks

Exploring factors affecting students’ continued Wiki use for individual and collaborative learning: An extended UTAUT perspective

What's your favorite blend? : Analyzing source and channel choices in business-to-government service interactions

Contact Info

Product

Resources

About