Social Influence and Knowledge Management Systems Use:  Evidence from Panel Data

Wang, Yinglei; Meister, Darren; Gray, Peter

doi:10.25300/misq/2013/37.1.13

Cited by 127 publications

(87 citation statements)

References 69 publications

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“…However, these models do not guarantee a durable success of KMS, because the usage of KMS is affected by various factors, e.g. social influence (Wang et al, 2013). In contrast to KMS, ICs as well as virtual communities (VC) depend on the development of user commitment and a sense of community (Leimeister and Krcmar, 2005).…”

Section: Introductionmentioning

confidence: 99%

Lean on Diffusion Theory to Make Ideas-Communities Fly The Case of DATEV

Hartmann

2014

SSRN Journal

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Section: Introductionmentioning

confidence: 99%

Lean on Diffusion Theory to Make Ideas-Communities Fly The Case of DATEV

Hartmann

2014

SSRN Journal

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“…Support from top management may be important Lin, 2014) as may be the organization's social and cultural values Lin, 2014). We do not know whether social influences from one's direct superiors influences use or not (Wang et al, 2013), but empowering leadership that shares power with employees and motivates to act with increased autonomy can increase the adoption and use of a KMS (Kuo, Lai, & Lee, 2011;Singh, 2008). …”

Section: Knowledge Management Systemsmentioning

confidence: 99%

“…Some empirical evidence suggests that social norms influence intent to use technology (Gallivan, Spitler, & Koufaris, 2005;Jasperson et al, 2005;Lucas & Spitler, 1999;Zhang, Prybutok, & Koh, 2006), including KMS peers (Wang et al, 2013). Most research suggests that norms have a fleeting effect (e.g., Stafford & Turan, 2011).…”

Section: Hypotheses: the Structures That Influence Loyal Usementioning

confidence: 99%

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Demystifying the Link between Social Media Addiction and Sharing without Verification: The Role of Absentmindedness and Wellbeing

Dennis¹,

Clay²,

Ko³

2024

THCI

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Abstract:In this paper, we argue that one can understand the adoption and routine use of enterprise systems by examining the fit between the system and the social structures of the organization. We develop a model of the adoption and longterm use of enterprise systems based on sociological concepts (Lord Anthony Giddens' structuration theory) rather than the usual cognitive psychology concepts. We focus on the adoption and use of three versions of an enterprise KMS to support sales representatives at a multinational pharmaceutical firm. Our first study (a five-year case study of the KMS that went through one failed deployment and two successful ones) shows that the structures of signification, legitimation, and domination all influence loyal use, although domination may be less important. Our second study (a survey of 893 users at the firm) shows that the structures of signification, legitimation, and domination explain about 50 percent of the variance in ongoing loyal use but that their relative importance depends on the job experience of the users: signification and legitimation influence novices more, and signification and domination influence experts more. We believe that this parsimonious three-factor model offers a useful approach for future research and practice.

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“…. Johnson (2008) Assimilation of electronic channel Efficiency, learning orientation Gupta et al (2004) Channel-switching Risk perception, search price, evaluation effort, waiting time Ansari et al (2008) Channel migration Purchase, income, age, catalogs Our study Multichannel access Social influence, rating effects of social influence in the adoption or use of new technology and systems (Wang, Meister, & Gray, 2013). In marketing, many scholars explored the effects of social influence between customers in the decision to adopt a new product (Risselada et al, 2014;Shen, Zhang, & Zhao, 2016), demonstrating the value of investment in advertisement to enhance social influence (Terlutter & Capella, 2013).…”

Section: Research Frameworkmentioning

confidence: 99%

Exploring the social influence of multichannel access in an online health community

Luo

Chen

et al. 2017

Asso for Info Science & Tech

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Social influence has a great impact on human behavior, which has been widely investigated in various research fields. Even so, it has rarely been investigated in the online health community. In this paper, we focus on the multichannel access in online health communities, defining social influence as the average degree of multichannel access to a physician's colleagues. Based on the multinomial logistic regression model, we examined the direct effects of social influence and patients' rating to multichannel access. In addition, we explored the moderating effect of social influence on the relationship between patients' rating and multichannel access in online health communities. The results of the experiment and robustness testing support the propositions that social influence and patients' rating significantly and positively affect multichannel access in an online health community. The moderating effect of social influence is negative and significantly influences the accessible channels provided by the focal physician. This research contributes to the literature concerning online health communities, social influence, and multichannel access; it also has practical implications.

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Social Influence and Knowledge Management Systems Use: Evidence from Panel Data

Cited by 127 publications

References 69 publications

Lean on Diffusion Theory to Make Ideas-Communities Fly The Case of DATEV

Lean on Diffusion Theory to Make Ideas-Communities Fly The Case of DATEV

Demystifying the Link between Social Media Addiction and Sharing without Verification: The Role of Absentmindedness and Wellbeing

Exploring the social influence of multichannel access in an online health community

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