Types Of Social Influence Research Articles

Digital, Social Micro-Interventions to Promote Physical Activity Among Midlife Adults With Elevated Cardiovascular Risk: An Ambulatory Feasibility Study With Momentary Randomization.

Although regular physical activity (PA) mitigates the risk for cardiovascular disease (CVD) during midlife, existing PA interventions are minimally effective. Harnessing social influences in daily life shows promise: digital micro-interventions could effectively engage these influences on PA and require testing. This feasibility study employed ecological momentary assessment with embedded micro-randomization to activate two types of social influences (i.e., comparison, support; NCT04711512). Midlife adults (N = 30, MAge = 51, MBMI = 31.5 kg/m2, 43% racial/ethnic minority) with ≥1 CVD risk conditions completed four mobile surveys per day for 7 days while wearing PA monitors. After 3 days of observation, participants were randomized at each survey to receive 1 of 3 comparison micro-interventions (days 4-5) or 1 of 3 support micro-interventions (days 6-7). Outcomes were indicators of feasibility (e.g., completion rate), acceptability (e.g., narrative feedback), and potential micro-intervention effects (on motivation and steps within-person). Feasibility and acceptability targets were met (e.g., 93% completion); ratings of micro-intervention helpfulness varied by intervention type and predicted PA motivation and behavior within-person (srs=0.16, 0.27). Participants liked the approach and were open to ongoing micro-intervention exposure. Within-person, PA motivation and behavior increased from baseline in response to specific micro-interventions (srs=0.23, 0.13), though responses were variable. Experimental manipulation of social influences in daily life is feasible and acceptable to midlife adults and shows potential effects on PA motivation and behavior. Findings support larger-scale testing of this approach to inform a digital, socially focused PA intervention for midlife adults.

Exploring Nurses' Behavioural Intention to Adopt AI Technology: The Perspectives of Social Influence, Perceived Job Stress and Human-Machine Trust.

This study examines how social influence, human-machine trust and perceived job stress affect nurses' behavioural intentions towards AI-assisted care technology adoption from a new perspective and framework. It also explores the interrelationships between different types of social influence and job stress dimensions to fill gaps in academic literature. A quantitative cross-sectional study. Five hospitals in Taiwan that had implemented AI solutions were selected using purposive sampling. The scales, adapted from relevant literature, were translated into Chinese and modified for context. Questionnaires were distributed to nurses via snowball sampling from May 15 to June 10, 2023. A total of 283 valid questionnaires were analysed using the partial least squares structural equation modelling method. Conformity, obedience and human-machine trust were positively correlated with behavioural intention, while compliance was negatively correlated. Perceived job stress did not significantly affect behavioural intention. Compliance was positively associated with all three job stress dimensions: job uncertainty, technophobia and time pressure, while obedience was correlated with job uncertainty. Social influence and human-machine trust are critical factors in nurses' intentions to adopt AI technology. The lack of significant effects from perceived stress suggests that nurses' personal resources mitigate potential stress associated with AI implementation. The study reveals the complex dynamics regarding different types of social influence, human-machine trust and job stress in the context of AI adoption in healthcare. This research extends beyond conventional technology acceptance models by incorporating perspectives on organisational internal stressors and AI-related job stress. It offers insights into the coping mechanisms during the pre-adaption AI process in nursing, highlighting the need for nuanced management approaches. The findings emphasise the importance of considering technological and psychosocial factors in successful AI implementation in healthcare settings. No Patient or Public Contribution.

Social influence and external feedback control in humans.

This article aims to unravel the dynamics of social influence by examining the processes that occur when one person is the target of another's influence. We hypothesized that these processes are part of a feedback loop system in an individual. This loop involves the situation (input), a goal state (reference), a comparator, a selection mechanism, a feedback predictor, and an action (output). Each element can become the target of social influence, and different types of social influence can be classified and explained by how these elements are targeted. For instance, attempting to persuade another person with strong arguments targets the goal state of the affected individual, while obedience targets the selection mechanism, and violence targets the action. In summary, this article aims to categorize, order, and explain phenomena in social influence research using a feedback loop framework focusing on the influenced individual.

Social influence and external feedback control in humans

This article aims to unravel the dynamics of social influence by examining the processes that occur when one person is the target of another’s influence. We hypothesized that these processes are part of a feedback loop system in an individual. This loop involves the situation (input), a goal state (reference), a comparator, a selection mechanism, a feedback predictor, and an action (output). Each element can become the target of social influence, and different types of social influence can be classified and explained by how these elements are targeted. For instance, attempting to persuade another person with strong arguments targets the goal state of the affected individual, while obedience targets the selection mechanism, and violence targets the action. In summary, this article aims to categorize, order, and explain phenomena in social influence research using a feedback loop framework focusing on the influenced individual.

Collective Behaviours: Mediation Mechanisms Underlying the Influence of Descriptive and Injunctive Norms

Conformity to descriptive and injunctive norms has been explained by informational and normative social influence. We argue that in addition to these two types of social influence, outcome expectancy can mediate descriptive norms’ impact on people’s intentions in the case of collective behaviours such as hand washing to prevent virus spread. Two studies manipulate norm type (descriptive vs injunctive) and norm level (low vs high) and show their effects on intention to perform the behaviour. In Study 1 (N = 216), outcome expectancy was positively influenced by descriptive norm and was associated with intention. In Study 2 (N = 731), outcome expectancy was influenced by descriptive but also by injunctive norm. Similar to Study 1, outcome expectancy was significantly associated with intention. Our data support the idea outcome expectancy is an important antecedent of intention and an additional mechanism underlying the effects of descriptive norms and, in some instances, injunctive norms.

Social influence and external feedback control in humans

This article aims to describe social influence by elucidating the cognitive, affective, and motivational processes that occur when an individual experiences an attempt at being influenced. We hypothesized that these processes are part of a feedback loop system in an individual. This loop involves the situation (input), a goal state (reference), a comparator, a selection mechanism, a feedback predictor, and an action (output). Each element can become the target of social influence, and different types of social influence can be classified and explained by how these elements are targeted. For instance, attempting to persuade another person with strong arguments targets the goal state of the affected individual, while coercion targets the selection mechanism, and violence targets the action. In summary, this article aims to categorize, order, and explain phenomena in social influence research using a feedback loop framework focusing on the influenced individual.

Exploring the determinants of continuous intention in TikTok from the perspective of social influence: a mixed approach of SEM and fsQCA

PurposeThis study embraces the call for exploring the determinants of continuous intention in TikTok. Taking the perspective of social influence, this study not only tries to explore the contextual sources of two types of social influence but also aims to unveil the influence mechanism of how social influence affects TikTok viewers’ continuous intention.Design/methodology/approachThis study empirically analyzes how TikToker attractiveness, co-viewer participation, platform reputation and content appeal affect informative and normative social influence and then lead to the continuous intention of TikTok. Based on 547 valid survey data, this study adopts a mixed analytical approach for data analysis by integrating structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA).FindingsSEM results unveil that content appeal is the most critical antecedent of informational social influence, while the TikToker attractiveness and platform reputation have no effect on it. Differently, all four external sources positively lead to normative social influence. Among them, content appeal and co-viewer participation influence the most. The influences of both two types of social influence on continuous intention are demonstrated. FsQCA results reveal seven alternative configurations that are sufficient for influencing continuance intention and further complement and reinforce the SEM findings.Originality/valueAddressing the critical contextual elements of TikTok, this study explores and confirms the sources which may engender social influence. The authors also demonstrate the critical role of social influence in affecting TikTok viewers’ continuous intentions by the hybrid analytical approach, which contributes to existing academic literature and practitioners.

Green hotel selection: the effects of social learning and eco-labels

Diseño/metodología/enfoque (límite 100 palabras)El estudio utilizó un método experimental online entre sujetos con 199 encuestados, utilizado sólo en alrededor del 15% de los artículos de turismo publicados. Además, este método ofrecía características de fiabilidad y control.Objetivo (límite 100 palabras)El consumo sostenible es una vía crucial para el turismo sostenible. Por ello, este estudio investiga el efecto individual y combinado del aprendizaje social y las etiquetas ecológicas en la selección de hoteles ecológicos.Conclusiones (límite 100 palabras)El aprendizaje social y las ecoetiquetas por separado no pudieron influir en los turistas para que seleccionaran hoteles ecológicos. Sin embargo, la interacción del aprendizaje social del rendimiento positivo con las ecoetiquetas podría influir potencialmente en la selección de hoteles ecológicos por parte de los turistas. Por lo tanto, la interiorización del rendimiento de los hoteles ecológicos a partir de fuentes de confianza y la validación externa provocan cambios de comportamiento entre los turistas para seleccionar hoteles ecológicos. El estudio ofrece un nuevo modelo basado en el aprendizaje social para entender el consumo sostenible.Limitaciones/implicaciones de la investigación (límite 100 palabras)El estudio puede utilizar otros tipos de influencias sociales y puede ampliarse a otros entornos ecológicos.Implicaciones prácticas (límite 100 palabras)Proporciona ideas para la formulación de políticas y promociones a los directores de hoteles y a los promotores del turismo sostenible para comercializar hoteles ecológicos.Originalidad/valor (límite 100 palabras)Se aleja de la teoría tradicional y tiende un puente entre el aprendizaje social de los resultados a partir de fuentes relacionadas, la garantía de las entidades gobernantes y el consumo sostenible. El efecto de la interacción entre la influencia social y la etiqueta ecológica en la selección de hoteles ecológicos es un hallazgo novedoso. Además, introduce varios niveles de aprendizaje social en el debate sobre el consumo sostenible.

Consumer susceptibility to social influence in new product diffusion networks: how does network location matter?

Purpose This study aims to empirically investigate how susceptibility to social influence in new product adoption varies with one’s structural location in a social network. Design/methodology/approach The social network data were collected based on a sociometric network survey with 589 undergraduate students. Social network analysis and ordinary least squares regression analyses were used to test the hypotheses. Findings This study finds that consumers with high degree centrality (i.e. hubs) who have a large number of connections to others and consumers with high betweenness centrality (i.e. bridges) who connect otherwise distant groups in social networks are both less sensitive to informational influence from others. More importantly, the authors find evidence that consumers with moderate levels of degree/betweenness centrality are more susceptible to normative influence and status competition than those with low or high degree/betweenness centrality. The inverse-U patterns in the above relations are consistent with middle-status conformity and anxiety. Research limitations/implications This research complements social influence and new product diffusion research by documenting important contingencies (i.e. network locations) in consumer susceptibility to different types of social influence from a social network perspective. Practical implications The findings will assist marketers to leverage social influence by activating relevant social ties with effective messages in their network marketing strategies. Originality/value This research provides a better understanding of the mechanisms driving susceptibility to social influence in new product diffusion.

Susceptibility to prosocial and antisocial influence in adolescence

IntroductionAdolescents are particularly susceptible to social influence and previous studies have shown that this susceptibility decreases with age. The current study used a cross-sectional experimental paradigm to investigate the effect of age and puberty on susceptibility to both prosocial and antisocial influence. MethodsParticipants (N = 520) aged 11–18 from London and Cambridge (United Kingdom) rated how likely they would be to engage in a prosocial (e.g. “help a classmate with their work”) or antisocial (e.g. “make fun of a classmate”) act. They were then shown the average rating (in fact fictitious) that other adolescents had given to the same question, and were then asked to rate the same behaviour again. ResultsBoth prosocial and antisocial influence decreased linearly with age, with younger adolescents being more socially influenced when other adolescents’ ratings were more prosocial and less antisocial than their own initial rating. Both antisocial and prosocial influence significantly decreased across puberty for boys but not girls (independent of age). ConclusionsThese findings suggest that social influence declines with increasing maturity across adolescence. However, the exact relationship between social influence and maturity is dependent on the nature of the social influence and gender. Understanding when adolescents are most susceptible to different types of social influence, and how this might influence their social behaviour, has important implications for understanding adolescent social development.

Social influence based community detection in event-based social networks

In this paper, we focus on the problem of discovering internally connected communities in event-based social networks (EBSNs) and propose a community detection method by utilizing social influences between users. Different from traditional social network, EBSNs contain different types of entities and links, and users in EBSNs have more complex behaviours. This leads to poor performance of the traditional social influence computation method in EBSNs. Therefore, to quantify the pairwise social influence accurately in EBSNs, we first propose to compute two types of social influences, i.e., structure-based social influence and behaviour-based social influence, by utilizing the online social network structure and offline social behaviours of users. In particular, based on the specific features of EBSNs, the similarities of user preference on three aspects (i.e., topics, regions and organizers) are utilized to measure the behaviour-based social influence. Then, we obtain the unified pairwise social influence by combining these two types of social influences through a weight function. Next, we present a social influence based community detection algorithm which is referred to as SICD. In SICD, inspired by the nonlinear feature learning ability of the autoencoder, we first devise a neighborhood based deep autoencoder algorithm to obtain nonlinear community-oriented latent representations of users, and then utilize the k-means algorithm for community detection. Experimental results conducted on real-world dataset show the effectiveness of our proposed algorithm.

Network Autocorrelation Modeling: Bayesian Techniques for Estimating and Testing Multiple Network Autocorrelations

The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social influence are present simultaneously and can be modeled using various connectivity matrices. Often, researchers have expectations about the order of strength of these different influence mechanisms. However, currently available methods cannot be applied to test a specific order of social influence in a network. In this article, the authors first present flexible Bayesian techniques for estimating network autocorrelation models with multiple network autocorrelation parameters. Second, they develop new Bayes factors that allow researchers to test hypotheses with order constraints on the network autocorrelation parameters in a direct manner. Concomitantly, the authors give efficient algorithms for sampling from the posterior distributions and for computing the Bayes factors. Simulation results suggest that frequentist properties of Bayesian estimators on the basis of noninformative priors for the network autocorrelation parameters are overall slightly superior to those based on maximum likelihood estimation. Furthermore, when testing statistical hypotheses, the Bayes factors show consistent behavior with evidence for a true data-generating hypothesis increasing with the sample size. Finally, the authors illustrate their methods using a data set from economic growth theory.

A contemporary review of three types of social influence in consumer psychology

AbstractThis review synthesizes the social presence literature that has been conducted in consumer psychology over the past decade. It provides a framework for understanding the impact of other buyers and salespeople on a consumer's thoughts, feelings, and behaviors. The review is structured around three types of social influence: utilitarian, value‐expressive, and informational. Examples of how recent research has explored each type of influence are provided. In addition to integrating the work that has previously been conducted, this review also highlights future research directions for advancing the field's understanding of the role and impact of a social presence on consumers.

Studying the “Wisdom of Crowds” at Scale

In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been dubbed the “wisdom of the crowd”. However, due to the varying contexts, sample sizes, methodologies, and scope of previous studies, it has been difficult to gauge the extent to which conclusions generalize. To investigate this question, we carried out a large online experiment to systematically evaluate crowd performance on 1,000 questions across 50 topical domains. We further tested the effect of different types of social influence on crowd performance. For example, in one condition, participants could see the cumulative crowd answer before providing their own. In total, we collected more than 500,000 responses from nearly 2,000 participants. We have three main results. First, averaged across all questions, we find that the crowd indeed performs better than the average individual in the crowd—but we also find substantial heterogeneity in performance across questions. Second, we find that crowd performance is generally more consistent than that of individuals; as a result, the crowd does considerably better than individuals when performance is computed on a full set of questions within a domain. Finally, we find that social influence can, in some instances, lead to herding, decreasing crowd performance. Our findings illustrate some of the subtleties of the wisdom-of-crowds phenomenon, and provide insights for the design of social recommendation platforms.

Understanding changes and stability in the long-term use of technologies by seniors who are aging in place: a dynamical framework

BackgroundIf technologies are to support aging in place, then it is important to develop fundamental knowledge on what causes stability and changes in the use of technologies by seniors. However, longitudinal studies on the long-term use of technologies that have been accepted into the home (i.e., post-implementation use) are very scarce. Many factors potentially could influence post-implementation use, including life events, age-related decline, changes in personal goal orientation, and various types of social influences. The aforementioned factors are likely to be interrelated, adding to the complexity. The goal of this study is to better understand changes and stability in the use of technologies by independent-living seniors, by using a dynamical systems theory approach.MethodsA longitudinal qualitative field study was conducted involving home visits to 33 community-dwelling seniors in the Netherlands, on three occasions (2012–2014). Interviews were held on technology usage patterns, including reasons for stable, increased, declined and stopped use. Technologies were included if they required electric power in order to function, were intended to be used in or around the home, and could support activities of daily living, personal health or safety, mobility, communication, and physical activity. Thematic analysis was employed, using constant case comparison to better understand dynamics and interplay between factors. In total, 148 technology use patterns by 33 participants were analyzed.ResultsA core of six interrelated factors was closely linked to the frequency of technology use: emotional attachment, need compatibility, cues to use, proficiency to use, input of resources, and support. Additionally, disruptive forces (e.g., social influences, competition with alternative means, changes of personal needs) could induce change by affecting these six factors. Furthermore, long-term technology use was in some cases more resilient to disruption than in other cases. Findings were accumulated in a new framework: Dynamics In Technology Use by Seniors (DITUS).ConclusionsSimilar to aging, the use of technologies by older people is complex, dynamic and personal. Periods of stability and change both occur naturally. The DITUS framework can aid in understanding stability and instability of technology use, and in developing and implementing sustainable technological solutions for aging in place.

Identifying Multiple Peer Influences on Smart Contract Adoption in Blockchain User Network

Smart contract brings more versatile functions in blockchain technology. However, its adoption rate is not as high as expected. Currently, there is not thorough study addressing the issue. To fill such gap, we propose to use social influence to explain smart contract adoption in blockchain user network. We explore whether and how different types of social influences, including centrality, direct peer adoption, and indirect peer adoption simultaneously affect an individual’s adoption decision of smart contracts. Our hypotheses are examined in the context of CryptoKitties adoption in the Ethereum network using the public dataset of Ethereum including 350 million transactions from over 20 million distinct accounts. The results show that the adoption of the software is positively affected by degree centrality and direct peer adoption and is negatively affected by indirect peer adoption. The results provide strong evidence of social influence on smart contract adoption through various mechanisms.

Quantifying the Impact of Social Influence on the Information Technology Implementation Process by Physicians: A Hierarchical Bayesian Learning Approach

Technology implementation at the individual level within an organization, after the organization has adopted the technology, has been an ongoing challenge in every field. In this study, we develop a hierarchical Bayesian learning model to examine the impact of social learning, through both targeted early adopter effects and general peer effects, and experiential learning on the information technology implementation process by physicians in a community health system. Our unique data allow us to disentangle the most common and challenging endogeneity issues associated with most social influence studies. We find that the experiential learning signal is more accurate than the social learning signals in the technology implementation process; and, between the two types of social learning signals studied here, targeted early adopter effects are much more informative than general peer effects. Furthermore, we experiment with several policy simulations to illustrate and quantify the two different types of social influence on this implementation process. The simulation results suggest that maintaining consistency in technology usage by targeted early adopters is more effective than increasing the frequency of their technology usage in reducing their colleagues’ perceptions of uncertainty about the new technology. More specifically, we find that technology implementation probability would increase: (a) by 15%, on average, by adding a targeted early adopter to a group without early adopters; (b) by 25% by adding peer effects to solo users; and (c) by 47% by adding early adopter effects to solo users. The model can be adapted and generalized to other similar settings that examine social influence on the technology implementation process and also provide quantifiable measures of the improvements that the interventions may produce.

Social influence in the global diffusion of alternative fuel vehicles – A meta-analysis

Alternative fuel vehicle technologies are needed to mitigate rising greenhouse gas emissions from transport. Social influence is integral to the diffusion of private vehicles which are highly visible and fulfil practical as well as social functions. This paper provides the first meta-analysis of empirical studies which measure the strength of social influence on consumer vehicle choice. A systematic literature review identified 21 studies that examined three types of social influence: interpersonal communication; neighbourhood effect; and conformity with social norms. A random effects meta-analysis found a significant and small to moderate effect of social influence on vehicle choices (r=0.241, p<0.001). The overall effect size did not vary significantly between types of social influence nor between types of vehicle (conventional or alternative fuel). However, further analysis using meta-regression found that heterogeneity in social influence effect size across studies was explained by differences in countries' cultural receptiveness to normative influence. These findings have important implications for policy and modelling analysis of alternative fuel vehicle adoption, for which diffusion is both a socially and culturally-mediated process.

Sources and impacts of social influence from online anonymous user reviews

Consumers increasingly rely on online reviews to make purchase decisions. However, the process through which consumers are influenced by online reviews is not well understood. To fill the gap, we apply the social influence theory to theoretically explain and analyze this opinion change process. Specifically, we identify antecedents and consequences of two types of social influence from online user reviews: informational and value-expressive influence. From a survey conducted in a controlled laboratory environment, we found that perceived review quality positively impacts informational influence, while perceived review quality, consistency, and social presence jointly impact value-expressive influence. Interestingly, informational influence impacts both perceived decision quality and perceived usefulness of the website, while value-expressive influence only impacts perceived usefulness of the website. Surprisingly, consumer characteristics, including prior product expertise and self-monitoring, do not have significant effects on the formation of social influence online.

Popularity or Proximity: Characterizing the Nature of Social Influence in an Online Music Community

We study social influence in an online music community. In this community, users can listen to and “favorite” (or like) songs and follow the favoriting behavior of their social network friends—and the community as a whole. From an individual user’s perspective, two types of information on peer consumption are salient for each song: total number of favorites by the community as a whole and favoriting by their social network friends. Correspondingly, we study two types of social influence: popularity influence, driven by the total number of favorites from the community as a whole, and proximity influence, due to the favoriting behavior of immediate social network friends. Our quasi-experimental research design applies a variety of empirical methods to highly granular data from an online music community. Our analysis finds robust evidence of both popularity and proximity influence. Furthermore, popularity influence is more important for narrow-appeal music compared to broad-appeal music. Finally, the two types of influence are substitutes for one another, and proximity influence, when available, dominates the effect of popularity influence. We discuss implications for design and marketing strategies for online communities, such as the one studied in this paper.