Social influence in technology adoption: taking stock and moving forward

Graf‐Vlachy, Lorenz; Buhtz, Katharina; König, Andreas

doi:10.1007/s11301-017-0133-3

Cited by 75 publications

(61 citation statements)

References 118 publications

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“…Moreover, the results show that users are happy about their use of a search engine that contributes to a more fair and equitable distribution of water resources. This result confirms previous studies [34,50] and demonstrates the influence of this emotion of happiness on the behavioral intention. The effect of social influence on habits (β = 0.130, t = 2.931), trust (β = 0.213, t = 4.429), and hedonic motivation (β = 0.105, t = 2.437) increases, suggesting that the users of Lilo, in their use of this search engine, experience more happiness and confidence, as well as have a tendency to rely on habit.…”

Section: Discussionsupporting

confidence: 93%

“…Social influence is the measure in which users understand that people in their environment have an influence on their decision making. Users might think that this environment wants them to use a technology, mobile device or an online search engine [49,50].…”

Section: Hypothesis 4 (H4) Facilitating Conditions Would Have a Posimentioning

confidence: 99%

“…In their work on online search engine adoption by users, Ling Tai [34] identified habit and hedonic motivations as two major factors that influence behavioral intention. Furthermore, Venkatesh, Thong, and Xu [50] analyzed the relationship between hedonic motivation and behavioral intention and found that, if the activity developed produces happiness to the user, this user will develop behavioral intention. Based on the above, our last hypothesis is as follows:…”

Section: Hypothesis 14 (H14) Trust Would Have a Positive Effect On Bmentioning

confidence: 99%

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Understanding User Behavioral Intention to Adopt a Search Engine that Promotes Sustainable Water Management

et al. 2018

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An increase in users’ online searches, the social concern for an efficient management of resources such as water, and the appearance of more and more digital platforms for sustainable purposes to conduct online searches lead us to reflect more on the users’ behavioral intention with respect to search engines that support sustainable projects like water management projects. Another issue to consider is the factors that determine the adoption of such search engines. In the present study, we aim to identify the factors that determine the intention to adopt a search engine, such as Lilo, that favors sustainable water management. To this end, a model based on the Theory of Planned Behavior (TPB) is proposed. The methodology used is the Structural Equation Modeling (SEM) analysis with the Analysis of Moment Structures (AMOS). The results demonstrate that individuals who intend to use a search engine are influenced by hedonic motivations, which drive their feeling of contentment with the search. Similarly, the success of search engines is found to be closely related to the ability a search engine grants to its users to generate a social or environmental impact, rather than users’ trust in what they do or in their results. However, according to our results, habit is also an important factor that has both a direct and an indirect impact on users’ behavioral intention to adopt different search engines.

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Section: Discussionsupporting

confidence: 93%

Section: Hypothesis 4 (H4) Facilitating Conditions Would Have a Posimentioning

confidence: 99%

Section: Hypothesis 14 (H14) Trust Would Have a Positive Effect On Bmentioning

confidence: 99%

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Understanding User Behavioral Intention to Adopt a Search Engine that Promotes Sustainable Water Management

et al. 2018

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“…Systematic literature reviews may be used to address conceptual intersection (e.g. Graf-Vlachy et al 2018) but also to address intersections (Block et al 2017). The intention was to use the highest ranked journals within MA research and the highest ranked journals within OM to find the journals that have the highest impact on prevailing research norms.…”

Section: The Initial Three Stepsmentioning

confidence: 99%

Across the great divide: a literature review of management accounting and operations management at the shop floor

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2018

Manag Rev Q

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Management accounting's ability to provide relevant information in production environments has long been discussed in the fields of management accounting (MA) and operations management (OM). Researchers from each field play a major part not only in disseminating their research results, but also in channelling their perceptions of management accounting in production environments through journal publications. The thesis of this paper is that via an examination of the paradigms, theories, and methods in the fields of MA and OM our understanding of the prevailing assumptions about management accounting in production environments in the academic community can be enhanced. The review shows a divide between the fields where the field of OM is oriented towards problem-solving, and the field of MA is more theory oriented. The review points out that the understanding of practice is a divider between the fields, but it also suggests that incorporation of practicing production members into research is a promising path forward. The paper then concludes that OM problematizes management accounting in production environments as a starting point for their research agenda and that both fields portrayal of management accounting in production environments need to be nuanced. There is a need to challenge the research expectations and to accept unconventional research methods to enhance knowledge about management accounting in production environments.

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“…The connections and interactions of individuals that comprise social networks are generally believed to impact decision-making in many domains including product selection and decision making in uncertain environments [1,2]. While there is general theoretical consensus that social influence, the phenomenon by which an individual's opinions, behaviors, and decisions are influenced by other people [3], facilitates product selection, the empirical literature is actually quite torn.…”

Section: Introductionmentioning

confidence: 99%

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

et al. 2020

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It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the "pattern" by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game decides among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make a similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.

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Social influence in technology adoption: taking stock and moving forward

Cited by 75 publications

References 118 publications

Understanding User Behavioral Intention to Adopt a Search Engine that Promotes Sustainable Water Management

Understanding User Behavioral Intention to Adopt a Search Engine that Promotes Sustainable Water Management

Across the great divide: a literature review of management accounting and operations management at the shop floor

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

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