Skip to main content

Social influence in technology adoption: taking stock and moving forward


Social influence has been shown to profoundly affect human behavior in general and technology adoption in particular. Over time, multiple definitions and measures of social influence have been introduced to the field of technology adoption research, contributing to an increasingly fragmented landscape of constructs that challenges the conceptual integrity of the field. Consequently, this paper sets out to review how social influence has been conceptualized in technology adoption research. In so doing, this paper attempts to inform researchers’ understanding of the construct, reconcile its myriad conceptualizations, constructively challenge extant approaches, and provide impulses for future research. A systematic review of the salient literature uncovers that extant interpretations of social influence are (1) predominantly compliance-based and as such risk overlooking identification- and internalization-based effects; (2) primarily targeted at the individual level and non-social technologies, thereby precluding the impact of socially enriched environments; and (3) heavily reliant on survey-based and US/China-centric samples, which jeopardizes the generalizability and predictive validity of the findings. Building upon these insights, this paper develops an integrated perspective on social influence in technology adoption research that encourages scholars to pursue a multi-theoretical understanding of social influence at the interface of users, social referents, and technology.

This is a preview of subscription content, access via your institution.

Fig. 1

(adapted from Burnkrant and Cousineau 1975, p. 207)

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. This condition was only applied to papers published up to and including 2011. All papers from 2012 onwards were reviewed individually to prevent the timeframe since publication to act as a limiting factor on the number of citations. Using the year 2011 as a cut-off allowed for a roughly stable number of papers per year in the final sample.

  2. Subsequently superseded by the UTAUT, in which the indirect effect of subjective norm was dropped and replaced by a social influence construct that includes the notion of support (Venkatesh et al. 2003).

  3. The UTAUT (Venkatesh et al. 2003) measures social influence based on a combination of two items related to subjective norm (Fishbein and Ajzen 1975) and two items related to social factors (Thompson et al. 1991).

  4. It is important to distinguish support as a form of social influence from support as a facilitating condition. The latter refers to objective factors in the environment that make an act easy to do, such as the provision of computer training and technical support in the context of technology acceptance (Thompson et al. 1991).

  5. Technology adoption scholars speak of “perceived” critical mass since it is difficult to determine the actual critical mass threshold for a specific technology, but individuals may have a subjective perception thereof (Cho 2011).

  6. Cho (2011) provides a good example for how to do this based on subjective norm and perceived critical mass.

  7. The sample of studies at the societal level is limited as cultural values and dimensions (e.g., Hofstede 1983) are not considered a social influence construct. For a review of culture in information systems research please refer to Leidner and Kayworth (2006).


References marked with an asterisk indicate papers included in the literature review

  • *Aboelmaged MG (2010) Predicting e-procurement adoption in a developing country: an empirical integration of technology acceptance model and theory of planned behaviour. Ind Manag Data Syst 110(3):392–414

  • Adler PS, Kwon S (2002) Social capital: prospects for a new concept. Acad Manag Rev 27(1):17–40

    Article  Google Scholar 

  • *Agarwal R, Animesh A, Prasad K (2009) Social interactions and the ‘digital divide’: explaining variations in internet use. Inf Syst Res 20(2):277–294

  • AIS (2011) Senior scholars’ basket of journals. Accessed 5 June 2016

  • Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211

    Article  Google Scholar 

  • *Akar E, Mardikyan S (2014) Analyzing factors affecting users’ behavior intention to use social media: twitter case. Int J Bus Soc Sci 5(11):85–95

  • Akerlof GA, Kranton RE (2000) Economics and identity. Q J Econ 115(3):715–753

    Article  Google Scholar 

  • Alberghini E, Cricelli L, Grimaldi M (2010) Implementing knowledge management through IT opportunities: definition of a theoretical model based on tools and processes classification. In: Proceedings of the European conference on intellectual capital, pp 21–33

  • *Al-Gahtani SS (2004) Computer technology acceptance success factors in Saudi Arabia: an exploratory study. J Glob Inf Technol Manag 7(1):5–29

  • Algesheimer R, Dholakia UM, Herrmann A (2005) The social influence of brand community: evidence from European car clubs. J Mark 69(3):19–34

    Article  Google Scholar 

  • *Al-Qeisi K, Dennis C, Hegazy A, Abbad M (2015) How viable is the UTAUT model in a non-western context? Int Bus Res 8(2):204–219

  • Asch SE (1953) Effects of group pressure upon the modification and distortion of judgments. In: Cartwright D, Zander A (eds) Group dynamics: research and Theory. Harper and Row, New York, pp 222–236

    Google Scholar 

  • Bagozzi RP (2007) The legacy of the technology acceptance model and a proposal for a paradigm shift. J Assoc Inf Syst 8(4):244–254

    Google Scholar 

  • *Bagozzi RP, Dholakia UM (2002) Intentional social action in virtual communities. J Interact Mark 16(2):2–21

  • *Bagozzi RP, Dholakia UM (2006) Open source software user communities: a study of participation in Linux user groups. Manag Sci 52(7):1099–1115

  • Bagozzi RP, Lee K (2002) Multiple routes for social influence: the role of compliance, internalization, and social identity. Soc Psychol Q 65(3):226–247

    Article  Google Scholar 

  • Baker WE (1990) Market networks and corporate behavior. Am J Sociol 96(3):589–625

    Article  Google Scholar 

  • *Baron S, Patterson A, Harris K (2006) Beyond technology acceptance: Understanding consumer practice. Int J Serv Ind Manag 17(2):111–135

  • Bélanger F, Carter L (2012) Digitizing government interactions with constituents: an historical review of e-government research in information systems. J Assoc Inf Syst 13(5):363–394

    Google Scholar 

  • Borgatti SP, Foster PC (2003) The network paradigm in organizational research: a review and typology. J Manag 29(6):991–1013

    Google Scholar 

  • Bourdieu P (1986) The forms of capital. In: Richardson JG (ed) Handbook of theory of Research for the Sociology of Education. Greenword Press, Westport, pp 242–258

    Google Scholar 

  • *Brown SA, Dennis AR, Venkatesh V (2010) Predicting collaboration technology use: integrating technology adoption and collaboration research. J Manag Inf Syst 27(2):9–53

  • *Bruque S, Moyano J, Eisenberg J (2009) Individual adaptation to IT-induced change: the role of social networks. J Manag Inf Syst 25(3):177–206

  • Burnkrant RE, Cousineau A (1975) Informational and normative social influence in buyer behavior. J Consum Res 2(3):206–215

    Article  Google Scholar 

  • Burton-Jones A, Gallivan MJ (2007) Toward a deeper understanding of system usage in organizations: a multilevel perspective. MIS Q 31(4):657–679

    Article  Google Scholar 

  • Burton-Jones A, Straub DW (2006) Reconceptualizing system usage: an approach and empirical test. Inf Syst Res 17(3):228–246

    Article  Google Scholar 

  • *Chan F, Thong J, Venkatesh V, Brown SA, Hu PJH, Tam K-Y (2010) Modeling citizen satisfaction with mandatory adoption of an e-government technology. J Assoc Inf Syst 11(10):519–549

  • *Chan S-C, Lu M-T (2004) Understanding internet banking adoption and use behavior: a Hong Kong perspective. J Glob Inf Manag 12(3):21–43

  • *Chatterjee S, Sarker S, Valacich JS (2015) The behavioral roots of information systems security: exploring key factors related to unethical IT use. J Manag Inf Syst 31(4):49–87

  • *Chau PYK, Hu PJ-H (2001) Information technology acceptance by individual professionals: a model comparison approach. Decis Sci 32(4):699–719

  • *Chau PYK, Hu PJ-H (2002) Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Inf Manag Amst 39(4):297–311

  • *Chen S-C, Chen H-H, Chen M-F (2009) Determinants of satisfaction and continuance intention towards self-service technologies. Ind Manag Data Syst 109(9):1248–1263

  • *Cheng YM (2011) Antecedents and consequences of e-learning acceptance. Inf Syst J 21(3):269–299

  • *Cheung W, Chang MK, Lai VS (2000) Prediction of internet and world wide web usage at work: a test of an extended Triandis model. Decis Support Syst 30(1):83–100

  • *Chiu C-M, Hsu M-H, Wang ETG (2006) Understanding knowledge sharing in virtual communities: an integration of social capital and social cognitive theories. Decis Support Syst 42(3):1872–1888

  • Cho H (2011) Theoretical intersections among social influences, beliefs, and intentions in the context of 3G mobile services in Singapore: decomposing perceived critical mass and subjective norms. J Commun 61(2):283–306

    Article  Google Scholar 

  • *Choi J, Geistfeld LV (2004) A cross-cultural investigation of consumer e-shopping adoption. J of Econ Psychol 25(6):821–838

  • Chow WS, Chan LS (2008) Social network, social trust and shared goals in organizational knowledge sharing. Inf Manag Amst 45(7):458–465

    Article  Google Scholar 

  • Chui M, Manyika J, Bughin J, Dobbs R, Roxburgh C, Sarrazin H, Sands G, Westergren M (2012) The social economy: unlocking value and productivity through social technologies. McKinsey Global Institute, New York

    Google Scholar 

  • Cialdini RB, Goldstein NJ (2004) Social influence: compliance and conformity. Annu Rev Psychol 55:591–621

    Article  Google Scholar 

  • Cialdini RB, Reno RR, Kallgren CA (1990) A focus theory of normative conduct: recycling the concept of norms to reduce littering in public places. J Pers Soc Psychol 58(6):1015–1026

    Article  Google Scholar 

  • Cialdini RB, Trost MR (1998) Social influence: social norms, conformity, and compliance. In: Gilbert D, Fiske ST, Lindzey G (eds) The handbook of social psychology, vol 2, 4th edn. McGraw-Hill, New York, pp 151–192

    Google Scholar 

  • Coleman JS (1990) Foundation of social theory. The Belknap Press, Cambridge

    Google Scholar 

  • Conner M, Armitage CJ (1998) Extending the theory of planned behavior: a review and avenues for further research. J Appl Soc Psychol 28:1429–1464

    Article  Google Scholar 

  • *Datta P (2011) A preliminary study of ecommerce adoption in developing countries. Inf Syst J 21(1):3–32

  • Delone WH, McLean E (1992) Information systems success: the quest for the dependent variable. Inf Syst Res 3(1):60–95

    Article  Google Scholar 

  • Delone WH, McLean E (2003) The Delone and McLean model of information system success: a ten year update. J Manag Inf Syst 19(4):9–30

    Article  Google Scholar 

  • Deutsch M, Gerard HB (1955) A study of normative and informational social influences upon individual judgment. J Abnorm Soc Psychol 51(3):629–636

    Article  Google Scholar 

  • *Devaraj S, Easley RF, Crant JM (2008) How does personality matter? Relating the five-factor model to technology acceptance and use. Inf Syst Res 19(1):93–105

  • *Dholakia UM, Bagozzi RP, Pearo LK (2004) A social influence model of consumer participation in network- and small-group-based virtual communities. Int J Res Mark 21(3):241–263

  • *Dickinger A, Arami M, Meyer D (2008) The role of perceived enjoyment and social norm in the adoption of technology with network externalities. Eur J Inf Syst 17(1):4–11

  • *Dinev T, Hu Q (2007) The centrality of awareness in the formation of user behavioral intention toward protective information technologies. J Assoc Inf Syst 8(7):386–408

  • *Elie-Dit-Cosaque C, Pallud J, Kalika M (2012) The influence of individual, contextual, and social factors on perceived behavioral control of information technology: a field theory approach. J Manag Inf Syst 28(3):201–234

  • *Faullant R, Fuller J, Matzler K (2012) Mobile audience interaction—explaining the adoption of new mobile service applications in socially enriched environments. Eng Manag Res 1(1):59–76

  • Fishbein M, Ajzen I (1975) Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley, Reading

    Google Scholar 

  • *Foon YS, Fah BCY (2011) Internet banking adoption in Kuala Lumpur: an application of UTAUT model. Int J Bus Manag 6(4):161–167

  • French JRP, Raven B (1959) The bases of social power. In: Cartwright D (ed) Studies in social power. Institute for Social Research, Ann Arbor, pp 150–167

    Google Scholar 

  • Friedkin NE (2004) Social cohesion. Annu Rev Sociol 30:409–425

    Article  Google Scholar 

  • Friedkin NE, Johnsen EC (1999) Social influence networks and opinion change. Adv Group Process 16(1):1–29

    Google Scholar 

  • Fulk J (1993) Social construction of communication technology. Acad Manag J 36(5):921–950

    Google Scholar 

  • Fulk J, Schmitz J, Steinfield C (1990) A social influence model of technology use. In: Fulk J, Steinfield CW (eds) Organizations and communication technology. Sage, Newbury Park, pp 117–142

    Chapter  Google Scholar 

  • *Fusilier M, Durlabhij S (2005) An exploration of student internet use in India: the technology acceptance model and the theory of planned behaviour. Campus-Wide Inf Syst 22(4):233–246

  • *Gallivan MJ, Spitler VK, Koufaris M (2005) Does information technology training really matter? A social information processing analysis of coworkers’ inflnence on IT usage in the workplace. J Manag Inf Syst 22(1):152–192

  • *Gao L, Bai X (2014) A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pac J Mark Logist 26(2):211–231

  • *Gounaris S, Koritos C (2008) Investigating the drivers of internet banking adoption decision. Int J Bank Mark 26(5):282–304

  • Goyal S (2007) Connections: an introduction to the economics of networks. Princeton University Press, Princeton

    Google Scholar 

  • Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):1360–1380

    Article  Google Scholar 

  • *Gupta B, Dasgupta S, Gupta A (2008) Adoption of ICT in a government organization in a developing country: an empirical study. J Strateg Inf Syst 17(2):140–154

  • *Guzzo T, Ferri F, Grifoni P (2014) ECA: an e-commerce consumer acceptance model. Int Bus Res 8(1):145–155

  • Hofstede GH (1983) The dimensions of national cultures in fifty countries and three regions. In: Deregowski JB, Daiurawiec S, Annis RC (eds) Explications in cross-cultural psychology. Swets and Zeitlinger, Lisse

    Google Scholar 

  • Hofstede GH (2015) The Hofstede Centre. Accessed 20 June 2015

  • *Hong S-J, Kar YT (2006) Understanding the adoption of multipurpose information appliances: the case of mobile data services. Inf Syst Res 17(2):162–179

  • *Hong S-J, Thong J, Moon J-Y, Tam K-Y (2008) Understanding the behavior of mobile data services consumers. Inf Syst Front 10(4):431–445

  • *Hsieh JJP-A, Rai A, Keil M (2008) Understanding digital inequality: comparing continued use behavioral models of the socio-economically advantaged and disadvantaged. MIS Q 32(1):97–126

  • *Hsieh JJP-A, Rai A, Keil M (2011) Addressing digital inequality for the socioeconomically disadvantaged through government initiatives: forms of capital that affect ICT utilization. Inf Syst Res 22(2):233–253

  • *Hsu CL, Lu HP (2004) Why do people play on-line games? An extended TAM with social influences and flow experience. Inf Manag Amst 41(7):853–868

  • *Ilie V, Van Slyke C, Green G, Lou H (2005) Gender differences in perceptions and use of communication technologies. Inf Res Manag J 18(3):13–31

  • Inkpen AC, Tsang EWK (2005) Social capital, networks, and knowledge transfer. Acad Manag Rev 30(1):146–165

    Article  Google Scholar 

  • *Irani Z, Dwivedi YK, Williams MD (2009) Understanding consumer adoption of broadband: an extension of the technology acceptance model. J Oper Res Soc 60:1322–1334

  • *Jaradat M-IRM, Al Rababaa MS (2013) Assessing key factor that influence on the acceptance of mobile commerce based on modified UTAUT. Int J Bus Manag 8(23):102–112

  • *Johnston AC, Warkentin M (2010) Fear appeals and information security behaviors: an empirical study. MIS Q 34(3):549–566

  • *Junglas I, Goel L, Abraham C, Ives B (2013) The social component of information systems: How sociability contributes to technology acceptance. J Assoc Inf Syst 14(10):585–616

  • Karahanna E, Limayem M (2000) E-mail and v-mail usage: generalizing across technologies. J Organ Comput Electron Commer 10(1):49–66

    Google Scholar 

  • Karahanna E, Straub DW, Chervany NL (1999) Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Q 23(2):183–213

    Article  Google Scholar 

  • Katz ML, Shapiro C (1985) Network externalities, competition, and compatibility. Am Econ Rev 75(3):424–440

    Google Scholar 

  • Kelman HC (1958) Compliance, identification, and internalization: three processes of attitude change. J Confl Resolut 2(1):51–60

    Article  Google Scholar 

  • *Khalifa M, Shen KN (2008) Explaining the adoption of transactional B2C mobile commerce. J Enterp Inf Manag 21(2):110–124

  • *Kim C, Jahng J, Lee J (2007) An empirical investigation into the utilization-based information technology success model: integrating task-performance and social influence perspective. J Inf Technol 22(2):152–160

  • King WR, He J (2006) A meta-analysis of the technology acceptance model. Inf Manag Amst 43(6):740–755

    Article  Google Scholar 

  • *Kleijnen M, Wetzels M, De Ruyter K (2004) Consumer acceptance of wireless finance. J Financ Serv Mark 8(3):206–217

  • Klein KJ, Dansereau F, Hall RJ (1994) Levels issues in theory development, data collection, and analysis. Acad Manag Rev 19(2):195–229

    Article  Google Scholar 

  • Kraut RE, Rice RE, Cool C, Fish RS (1998) Varieties of social influence: the role of utility and norms in the success of a new communication medium. Organ Sci 9(4):437–453

    Article  Google Scholar 

  • *Kvasny L, Keil M (2006) The challenges of redressing the divide: a tale of two US cities. Inf Syst J 16(1):23–53

  • *Lau A, Yen J, Chau PYK (2001) Adoption of on-line trading in the Hong Kong financial market. J Electron Commer Res 2(2):58–65

  • *Lee BC, Cho J, Hwang D (2013) An integration of social capital and tourism technology adoption: a case of convention and visitors bureaus. Tour Hosp Res 13(3):149–165

  • *Lee M (2009) Understanding the behavioural intention to play online games: an extension of the theory of planned behaviour. Online Inf Rev 33(5):849–872

  • *Lee Y-C (2006) An empirical investigation into factors influencing the adoption of an e-learning system. Online Inf Rev 30(5):517–541

  • Legris P, Ingham J, Collerette P (2003) Why do people use information technology? A critical review of the technology acceptance model. Inf Manag Amst 40(3):191–204

    Article  Google Scholar 

  • Leidner DE, Kayworth T (2006) A review of culture in information systems research: toward a theory of information technology culture conflict. MIS Q 30(2):357–399

    Article  Google Scholar 

  • *Lewis W, Agarwal R, Sambamurthy V (2003) Source of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Q 27(4):657–678

  • *Li D, Chau PYK, Lou H (2005) Understanding individual adoption of instant messaging: an empirical investigation. J Assoc Inf Syst 6(4):102–129

  • Li SS, Karahanna E (2015) Online recommendation systems in a B2C e-commerce context: a review and future directions. J Assoc Inf Syst 16(2):72–107

    Google Scholar 

  • *Li X, Hess TJ, Valacich JS (2008) Why do we trust new technology? A study of initial trust formation with organizational information systems. J Strateg Inf Syst 17(1):39–71

  • *Liang H, Wei KK, Xue Y (2010) Understanding the influence of team climate on IT use. J Assoc Inf Syst 11(8):414–432

  • *Liao S, Chou E-Y (2012) Intention to adopt knowledge through virtual communities: posters vs lurkers. Online Inf Rev 36(3):442–461

  • Lin N (2001) Social capital: a theory of social structure and action. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • *Lou H, Luo W, Strong DM (2000) Perceived critical mass effect on groupware acceptance. Eur J Inf Syst 9(2):91–103

  • *Lu HP, Hsiao K-L (2007) Understanding intention to continuously share information on weblogs. Internet Res 17(4):345–361

  • *Lu J, Liu C, Yu C-S, Yao JE (2003) Exploring factors associated with wireless internet via mobile technology acceptance in Mainland China. Commun Int Inf Manag Assoc 3(1):101–120

  • *Lu J, Yu C-S, Liu C, Yao JE (2003) Technology acceptance model for wireless Internet. Internet Res 13(3):206–222

  • *Lu J, Yao JE, Yu C-S (2005) Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. J Strateg Inf Syst 14(3):245–268

  • *Macredie RD, Mijinyawa K (2011) A theory-grounded framework of Open Source Software adoption in SMEs. Eur J Inf Syst 20(2):237–250

  • *Magni M, Angst CM, Agarwal R (2013) Everybody needs somebody: the influence of team network structure on information technology use. J Manag Inf Syst 29(3):9–42

  • Malhotra Y, Galletta D (2005) A multidimensional commitment model of volitional systems adoption and usage behavior. J Manag Inf Syst 22(1):117–151

    Article  Google Scholar 

  • *Mardikyan S, Beşiroğlu B, Uzmaya G (2012) Behavioral intention towards the use of 3G technology. Commun IBIMA 2012:1–10

  • Markus ML (1990) Toward a critical mass theory of interactive media. In: Fulk J, Steinfield C (eds) Organizations and communication technology. Sage, Newbury Park, pp 194–218

    Chapter  Google Scholar 

  • Markus ML, Robey D (1988) Information technology and organizational change: causal structure in theory and research. Manag Sci 34:583–598

    Article  Google Scholar 

  • Mason WA, Conrey FR, Smith ER (2007) Situating social influence processes: dynamic, multidirectional flows of influence within social networks. Pers Soc Psychol Rev 11(3):279–300

    Article  Google Scholar 

  • Mathieson K (1991) Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Inf Syst Res 2(3):173–191

    Article  Google Scholar 

  • *McCoy S, Everard A, Jones B (2005) An examination of the technology acceptance model in Uruguay and the US: a focus on culture. J Glob Inf Technol Manag 8(2):27–45

  • McCoy S, Galletta D, King WR (2007) Applying TAM across cultures: the need for caution. Eur J Inf Syst 16(1):81–90

    Article  Google Scholar 

  • Merton RK, Sztompka P (1996) On social structure and science. University of Chicago Press, Chicago

    Google Scholar 

  • *Montazemi AR, Siam JJ, Esfahanipour A (2008) Effect of network relations on the adoption of electronic trading systems. J Manag Inf Syst 25(1):233–266

  • Moore GC, Benbasat I (1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf Syst Res 2(3):192–222

    Article  Google Scholar 

  • *Morris MG, Venkatesh V (2000) Age differences in technology adoption decisions: implications for a changing work force. Pers Psychol 53(2):375–403

  • Morris MW, Hong Y, Chiu C, Liu Z (2015) Normology: integrating insights about social norms to understand cultural dynamics. Organ Behav Hum Decis Process 129:1–13

    Article  Google Scholar 

  • Moscovici S, Mugny G, Van Avermaet E (1985) Perspectives on minority influence. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • *Mutlu S, Ergeneli A (2012) Electronic mail acceptance evaluation by extended technology acceptance model and moderation effects of espoused national cultural values between subjective norm and usage intention. Intellect Econ 6(2):7–28

  • Nahapiet J, Ghoshal S (1998) Social capital, intellectual capital, and the organizational advantage. Acad Manag Rev 23(2):242–266

    Article  Google Scholar 

  • *Neufeld DJ, Dong L, Higgins C (2007) Charismatic leadership and user acceptance of information technology. Eur J Inf Syst 16(4):494–510

  • *Nysveen H, Pedersen PE, Thorbjørnsen H (2005a) Explaining intention to use mobile chat services: moderating effects of gender. J Consum Mark 22(5):247–256

  • *Nysveen H, Pedersen PE, Thorbjørnsen H (2005b) Intentions to use mobile services: antecedents and cross-service comparisons. J Acad Mark Sci 33(3):330–346

  • Oliveira T, Martins MF (2011) Literature review of information technology adoption models at firm level. Electron J Inf Syst Eval 14(1):110–121

    Google Scholar 

  • Parsons T (1951) The social system. Routledge & Kegan Paul, London

    Google Scholar 

  • Papadopoulos T, Stamati T, Nopparuch P (2013) Exploring the determinants of knowledge sharing via employee weblogs. Int J Inf Manag 33(1):133–146

    Article  Google Scholar 

  • *Pavlou PA, Fygenson M (2006) Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Q 30(1):115–143

  • *Peng G, Dey D, Lahiri A (2014) Healthcare IT adoption: An analysis of knowledge transfer in socioeconomic networks. J Manag Inf Syst 31(3):7–34

  • Pinsonneault A, Barki H, Gallupe RB, Hoppen N (1999) Electronic brainstorming: the illusion of productivity. Inf Syst Res 10(2):110–133

    Article  Google Scholar 

  • Pillutla MM, Chen XP (1999) Social norms and cooperation in social dilemmas: the effects of context and feedback. Organ Behav Hum Decis Process 78(2):81–103

    Article  Google Scholar 

  • *Plouffe CR, Vandenbosch M, Hulland J (2001) Intermediating technologies and multi-group adoption: a comparison of consumer and merchant adoption intentions toward a new electronic payment system. J Prod Innov Manag 18(2):65–81

  • Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88(5):879–903

    Article  Google Scholar 

  • *Rauniar R, Rawski G, Yang J, Johnson B (2014) Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. J Enterp Inf Manag 27(1):6–30

  • Rice RE, Aydin CE (1991) Attitudes toward new organizational technology: network proximity as a mechanism for social information processing. Adm Sci Q 36(2):219–244

    Article  Google Scholar 

  • Rice RE, Grant AE, Schmitz J, Torobin J (1990) Individual and network influences on the adoption and perceived outcomes of electronic messaging. Soc Netw 12(1):27–55

    Article  Google Scholar 

  • *Riquelme HE, Rios RE (2010) The moderating effect of gender in the adoption of mobile banking. Int J Bank Mark 28(5):328–341

  • Rogers EM (1995) Diffusion of innovations, 4th edn. Free Press, New York

    Google Scholar 

  • Rogers EM (2003) Diffusion of innovations, 5th edn. Free Press, New York

    Google Scholar 

  • Sarker S, Ahuja M, Sarker S, Kirkeby S (2011) The role of communication and trust in global virtual teams: a social network perspective. J Manag Inf Syst 28(1):273–310

    Article  Google Scholar 

  • *Sarker S, Valacich JS (2010) An alternative to methodological individualism: a non-reductionist approach to studying technology adoption in groups. MIS Q 34(4):779–808

  • *Sarker S, Valacich JS, Sarker S (2005) Technology adoption by groups: a valence perspective. J Assoc Inf Syst 6(2):37–71

  • Schau HJ, Gilly MC (2003) We are what we post? Self-presentation in personal web space. J Consum Res 30(3):385–404

    Article  Google Scholar 

  • Schepers J, Wetzels M (2007) A meta-analysis of the technology acceptance model: investigating subjective norm and moderation effects. Inf Manag 44:90–103

    Article  Google Scholar 

  • Scott S, Orlikowski WJ (2014) Entanglements in practice: performing anonymity through social media. MIS Q 38(3):873–893

    Article  Google Scholar 

  • Scott WR (1987) The adolescence of institutional theory. Adm Sci Q 32(4):493–511

    Article  Google Scholar 

  • Seidler J (1974) On using informants: a technique for collecting quantitative data and controlling measurement error in organization analysis. Am Sociol Rev 39(6):816–831

    Article  Google Scholar 

  • *Shen J (2012) Social comparison, social presence, and enjoyment in the acceptance of social shopping websites. J Electr Commer Res 13(3):198–212

  • *Shen XL, Cheung CMK, Lee MKO (2013) Perceived critical mass and collective intention in social media-supported small group communication. Int J Inf Manag 33(5):707–715

  • *Shen XL, Lee MKO, Cheung CMK, Chen H (2010) Gender differences in intentional social action: we-intention to engage in social network-facilitated team collaboration. J Inf Technol 25(2):152–169

  • Shibutani T (1955) Reference groups as perspectives. Am J Sociol 60(6):562–569

    Article  Google Scholar 

  • *Sledgianowski D, Kulviwat S (2009) Using social network sites: the effects of playfulness, critical mass and trust in a hedonic context. J Comput Inf Syst 49(4):74–83

  • *Son J, Sadachar A, Manchiraju S, Fiore AM, Niehm LS (2012) Consumer adoption of online collaborative customer co-design. J Res Interact Mark 6(3):180–197

  • Sproull L, Faraj S (1997) Atheism, sex, and databases: the net as a social technology. In: Kiesler S (ed) Culture of the Internet. Lawrence Erlbaum, Mahwah, pp 35–51

    Google Scholar 

  • *Srite M, Karahanna E (2006) The role of espoused national cultural values in technology acceptance. MIS Q 30(3):679–704

  • *Strader TJ, Ramaswami SN, Houle PA (2007) Perceived network externalities and communication technology acceptance. Eur J Inf Syst 16(1):54–65

  • Straub DW, Loch K, Evaristo R, Karahanna E, Srite M (2002) Toward a theory-based measurement of culture. Hum Factors Inf Syst 10(1):61–65

    Article  Google Scholar 

  • *Sun Y, Wang N, Guo X, Peng Z (2013) Understanding the acceptance of mobile health services: a comparison and integration of alternative models. Electron Commer Res 14(2):183–200

  • *Sykes TA, Venkatesh V, Gosain S (2009) Model of acceptance with peer support: a social network perspective to understand employees’ system use. MIS Q 33(2):371–393

  • Tajfel HE (1978) Differentiation between social groups: studies in the social psychology of intergroup relations. Academic Press, London

    Google Scholar 

  • Tan C-H, Sutanto J, Phang CW, Gasimov A (2014) Using personal communication technologies for commercial communications: a cross-country investigation of email and SMS. Inf Syst Res 25(2):307–327

    Article  Google Scholar 

  • *Thakur R, Srivastava M (2013) Customer usage intention of mobile commerce in India: an empirical study. J Indian Bus Res 5(1):52–72

  • Thompson RL, Higgins CA, Howell JM (1991) Personal computing: toward a conceptual model of utilization. MIS Q 15(1):124–143

    Article  Google Scholar 

  • *Titah R, Barki H (2009) Nonlinearities between attitude and subjective norms in information technology acceptance: a negative synergy? MIS Q 33(4):827–844

  • Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14:207–222

    Article  Google Scholar 

  • Triandis HC (1980) Values, attitudes, and interpersonal behavior. In: Howe HE (ed) Nebraska symposium on motivation. University of Nebraska Press, Lincoln, pp 195–259

    Google Scholar 

  • *Tsai H-T, Bagozzi RP (2014) Contribution behavior in virtual communities: cognitive, emotional, and social Influences. MIS Q 38(1):143–163

  • Turner JC (1991) Social influence. Mapping social psychology. Open University Press, Milton Keynes

    Google Scholar 

  • Turner M, Kitchenham B, Brereton P, Charters S, Budgen D (2010) Does the technology acceptance model predict actual use? A systematic literature review. Inf Softw Technol 52(5):463–479

    Article  Google Scholar 

  • Van Raaij EM, Schepers J (2008) The acceptance and use of a virtual learning environment in China. Comput Educ 50(3):838–852

    Article  Google Scholar 

  • *Van Slyke C, Ilie V, Lou H, Stafford T (2007) Perceived critical mass and the adoption of a communication technology. Eur J Inf Syst 16(3):270–283

  • *Venkatesh V, Morris MG, Ackerman PL (2000) A longitudinal field investigation of gender differences in individual technology adoption decision-making process. Organ Behav Hum Decis Process 83(1):33–60

  • *Venkatesh V, Brown SA (2001) A longitudinal investigation of personal computers in homes: adoption determinants and emerging challenges. MIS Q 25(1):71–102

  • Venkatesh V, Brown SA, Bala H (2013) Briding the qualitative-quantitative divide: guidelines for conducting mixed methods research in information systems. MIS Q 37(1):1–34

    Article  Google Scholar 

  • *Venkatesh V, Davis FD (2000) Theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204

  • *Venkatesh V, Morris MG (2000) Why don’t men ever stop to ask for direction? Gender, social influence and their role in technology acceptance and usage behaviour. MIS Q 24(1):115–137

  • *Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478

  • *Venkatesh V, Morris MG, Sykes TA, Ackerman PL (2004) Individual reactions to new technologies in the workplace: the role of gender as a psychological construct. J Appl Soc Psychol 34(3):445–467

  • *Venkatesh V, Sykes TA (2013) Digital divide initiative success in developing countries: a longitudinal field study in a village in India. Inf Syst Res 24(2):1–22

  • *Venkatesh V, Zhang X (2010) Unified theory of acceptance and use of technology: US vs. China. J Glob Inf Technol Manag 13(1):5–27

  • *Venkatesh V, Zhang X, Sykes TA (2011) ‘Doctors do too little technology’: a longitudinal field study of an electronic healthcare system implementation. Inf Syst Res 22(3):523–546

  • Wand Y, Weber R (1995) On the deep structure of information systems. Inf Syst J 5(3):203–223

    Article  Google Scholar 

  • *Wang E, Chou NP-Y (2014) Consumer characteristics, social influence, and system factors on online group-buying repurchasing intention. Electron Commer Res 15(2):119–132

  • *Wang Y, Meister DB, Gray P (2013) Social influence and knowledge management systems use: evidence from panel data. MIS Q 37(1):299–313

  • *Wasko MM, Faraj S (2005) Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Q 29(1):35–57

  • Wasserman S, Faust K (1994) Social network analysis: methods and applications. University Press, Cambridge

    Book  Google Scholar 

  • *Wattal S, Racherla P, Mandviwalla M (2010) Network externalities and technology use: a quantitative analysis of intraorganizational blogs. J Manag Inf Syst 27(1):145–174

  • Webster J, Watson R (2002) Analyzing the past to prepare for the future: Writing a review. MIS Q 26(2):xiii–xxiii

  • *White Baker E, Al-Gahtani SS, Hubona GS (2007) The effects of gender and age on new technology implementation in a developing country: testing the theory of planned behavior (TPB). Inf Technol People 20(4):352–375

  • *Williams MD, Slade EL, Dwivedi YK (2014) Consumers’ intentions to use e-readers. J Comput Inf Syst 54(2):66–77

  • Wilska T-A (2003) Mobile phone use as part of young people’s consumption styles. J Consum Policy 26(4):441–463

    Article  Google Scholar 

  • *Wu IL, Chen J-L (2005) An extension of trust and TAM model with TPB in the initial adoption of on-line tax: an empirical study. Int J Hum-Comput Stud 62(6):784–808

  • *Yang H (2013) Bon appétit for apps: young American consumers’ acceptance of mobile applications. J Comput Inf Syst 53(3):85–95

  • *Yang H, Liu H, Zhou L (2012) Predicting young Chinese consumers’ mobile viral attitudes, intents and behavior. Asia Pac J Mark Logist 24(1):59–77

  • *Yang K, Forney JC (2013) The moderating role of consumer technology anxiety in mobile shopping adoption: differential effects of facilitating conditions and social influences. Electron Commer Res 14:334–347

  • Yoo Y, Alavi M (2001) Media and group cohesion: relative influences on social presence, task participation, and group consensus. MIS Q 25(3):371–390

    Article  Google Scholar 

  • *Yu C-S (2012) Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Electron Commer Res 13:104–121

  • *Zhang X, Prybutok VR, Koh CE (2006) The role of impulsiveness in a TAM-based online purchasing behavior. Info Res Manag J 19(2):54–68

  • Zimbardo PG (1970) The human choice: individuation, reason, and order versus deindividuation, impulse, and chaos. In: Arnold WJ, Levine D (eds) Nebraska symposium on motivation. University of Nebraska Press, Lincoln, pp 237–307

    Google Scholar 

Download references


We would like to thank Hendrike Werwigk for editorial support. We further acknowledge helpful comments from participants of the 25\({\mathrm{th}}\) European Conference on Information Systems.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Lorenz Graf-Vlachy.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (xlsx 44 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Graf-Vlachy, L., Buhtz, K. & König, A. Social influence in technology adoption: taking stock and moving forward. Manag Rev Q 68, 37–76 (2018).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Social influence
  • Subjective norm
  • Technology adoption
  • Technology acceptance model
  • Information systems

JEL Classification

  • A12
  • O32
  • O33