Interactions between social network and IT innovation diffusion

Transcription

1 Interactions between social network and IT innovation diffusion Meng Shi University of Texas at Arlington Phone: (817) Abstract: Innovation diffusion theory provided a useful perspective on how to improve technology assessment, adoption and implementation. However, very few researches focused on the process of IT innovation diffusion through social network structure. By using IT diffusion framework, we reviewed information technology diffusion research and proposed a comprehensive model which combined social network perspective and TAM model to illustrate the procedures of IT diffusion through the established social network. The model provided an insight to better understand the IT adoption and thus facilitates the IT implementation. Keywords: IT diffusion, Social network, Innovation diffusion, Bandwagon theory, Interaction Introduction Diffusion theory provides tools, both quantitative and qualitative, for assessing the likely rate of diffusion of a technology, and additionally, identifies numerous factors that facilitate or hinder technology adoption and implementation. These factors include characteristics of the technology, characteristics of adopters, and the means by which adopters learn about and are persuaded to adopt the technology [1] (classical variables). However, scholars are more concerned with the question that why and when the potential adopters become the adopters. This paper attempts to establish a comprehensive model which reflects the characteristic of social network and thus provides answers to this question.

2 Our review focuses on information technology diffusion which is classified by two dimensions: locus of adoption and class of technology. Locus of adoption includes two kinds of locus: individual and organization; class of technology has two kinds of technology: Type I and Type II. Based on this two by two information diffusion framework, we summarize the research of each cell and then set the boundary of our study and establish the focal concepts. It then analyzes the body of articles that fell within the boundary of the study and presents a comprehensive model to explain the reasons to trig an information technology innovation and the procedures to track the information diffusion through the social network. Conceptual boundaries We frame our literature review within the research framework of IT diffusion. [2] (Robert G. Fichman, MIT Sloan School of Management,1992)-depicted in Fig1. Field1: Individual Adoption of Type 1 Technologies (Cell 1) Type 1 technologies were characterized by a lack of user interdependencies and a lack of a substantial knowledge burden on would-be adopters. Typical Type 1 technologies include singleuser hardware (e.g., microcomputers, laptops, portable terminals) and software (e.g., word processing, spreadsheets). These technologies were qualified as independent-use technologies since they were intended to facilitate self contained tasks performed by individual users. For studies in this field, the researchers focused on the investigating the situations where individual adopters apparently had wide autonomy in the adoption decision. Brancheau and Wetherbe found that the characteristics (age, education, exposure to media, interpersonal communication exposure, opinion leadership, external social participation, etc.) of early and late adopters were different as well as communication channel types (mass media or interpersonal) and sources(external or internal to company). [3]

3 In Davis study (1989) ease of use appeared to be a causal antecedent of usefulness, with little direct effect on use. [4] Davis,Bagozzi and Warshaw(1989)found that Perceived usefulness and ease of use had a significant direct effect on behavioral intentions, over and above their effect transmitted through the mediating attitude construct. Behavioral intention to use was significantly related to actual self reported use. [5] Marlene E.Burkhardt and Daniel J. Brass (1990) found that the following characteristics of adopters would be significantly different between the early and late adopters. (1) age (2) education level (3) previous computer training (4) attitudes toward computers, and (5) feelings of efficacy regarding computer use. [6] In general, the effect variables for individual adopters of type1 technology included: 1) perceived usefulness 2) characteristics of adopters3) communication channel and chance 4) managerial influences. Field2: Organizational Adoption of Type 1 Technologies (Cell 2) In this filed, the researchers concentrate on the diffusion of Type I technology on the organization level. Gatignon and Robertson(1989) confirmed that the decision maker s characteristics(preference for negative information and exposure to personal information sources) predicted future adoption. And Adoption was associated with high vertical integration and high supplier incentives in the supply industry, and high industry concentration and low competitive price intensity in the adopter industry. [7] James T.C.Teng, Varun Grover,and Wolfgang Guttler (2002) found that 1)technologies which were adopted by a maximal proportion of potential adopters provided either general support for all members in the organization or infrastructure that enhanced effectiveness of the organization

4 as whole2)there was a general positive relationship between the compatibility and IT adoption and a negative relationship between the complexity and IT adoption.3) technological tools with less set-up time will diffuse more fast. [8] In summary, the effect variables for diffusion of type 1 technology on organizational level include: 1) perceived usefulness and ease of use 2) organization characteristics 3) decision make characteristics 4) competitive effects 5)supply side factors 6) economic factors(price) 7) IT group characteristics. Figure 1 Review framework

5 Field3: Individual Adoption of Type 2 Technologies (Cell 3) Type 2 technologies, by contrast, were characterized by high knowledge barriers (e.g., structured systems analysis, stand-alone CAD drawing systems), or significant user interdependencies (e.g., , voice mail), or both (e.g., MRP, integrated CAD/CAM). In this field, Leonard-Barton(1987) Surveyed 145 programmers, analysts and supervisors in three sites within a natural resources firm and concluded that clients preferences, adopter attitudes, training in SSA strongly discriminate adopters from non-adopters. Years of experience, perceived accessibility of consulting, supervisor desires, and acquaintance with an advocate moderated discriminating. [9] James T.C.Teng (2002) also confirmed that the discrimination between the type1 technology (without knowledge barrier) and type 2 technology (with technology barrier)on the velocity and saturation of diffusion. [8] C. Haythornthwaite (2000) explored social support and community development among members of a computer-supported distance learning program. [10] Constant, D (1996) explored the process of giving and receiving technical advice over an organizational computer network and suggested how this process could lead to useful advice based on weak ties and organization motivation theory. [11] Field4: Organizational Adoption of Type 2 Technologies (Cell4) In this filed, the researchers concentrated on the diffusion of Type 2 technology on the organizational level. Ball, Dambolena,Hennessey (1987)concluded that Organizations with high R&D commitment and a large number of engineers and scientists in management were more likely to be early

6 adopters based on Questionnaires from 24 members of the Boston Chapter of the Society for Information Management. [15] Cooper and Zmud(1990)found that high task-technology compatibility (e.g., continuous manufacturing methods, make -to-stock marketing strategies) and low technological complexity(e.g., fewer parts per bill-of-material and per finished good) positively related to MRP adoption, but not infusion. [16] James T.C.Teng(2002) confirmed that the diffusion of innovations of IT yielded the Brass model (general S Shape). [8] Zmud(1982)concluded that Centralization was positively associated with initiation of compatible administrative innovations. And formalization was positively correlated with adoption of incompatible technical innovations. [9] In general, the researchers tried to uncover the different characteristics of innovation adoption organizations as well as the underlying reasons and processes of adopting innovations of IT on the organizational level. Being framed by this model, our research focused on Individual Adoption of Type 2 Technologies (Cell 3). In this field, the researchers were concerned with the technologies strongly subjected to network and thus concentrated the role of critical mass variables (e.g., early adopter incentives, level of community wide adoption) on individual adoption decisions; such researches provided a micro-level basis for presuming macro-level outcomes (e.g., distinctive patterns of adoption). However, the interdependent among adopters and non-adopters and the effects of such interdependent had been neglected given many researchers advocated interactions between the social network and the innovation adoption. [1,12,13,14]

7 Our research focused on the interactions between the social network and the innovations of IT adoptions on the individual adoption level. While the organization as a whole made the initial adoption decision for such technologies, the actions of individual adopters--e.g., how enthusiastically they embraced the innovation--can be expected to have a large impacts on the implementation process. And the extent of such impact was also discussed. Theory background Individual Adoption and Use of Information Systems Individual adoption was one of the richest streams of IS research, with several models explaining the key dependent variables of interest, that is, behavioral intention to use a system and system use. Research in this stream was originally rooted in psychology research, with the technology acceptance model (TAM; Davis et al. 1989) being the most influential model. However, the unified theory of acceptance and use of technology (UTAUT) which inherited the TAM model could only explain about 70 percent of the variance in behavioral intention to use a system, and about 40 percent of the variance in system use. Obviously, more underlying factors should be illustrated besides the individual intentions. We were going to explore the unknown factors based on social network perspective. Social Network Perspective The social network perspective draws on the patterns of interactions and exchanges within social units in which an actor is embedded to explain outcomes experienced by other actors (Borgatti and Foster 2003). In this perspective, an employee s position in a social network was linked to performance (Ahuja et al. 2003) and provided advantages. We adopted bandwagon theory to represent the social influence on the individual decisions. Bandwagon effect, also known as "cromo effect" and

8 closely related to opportunism, is the observation that people often do and believe things because many other people do and believe the same things. Next, we were going to put forward our proposals based on the two perspectives and previous literatures. Proposal development Whether or not the user adopt a new innovation was a function of the social context in which they act and speak [24]. It was also likely, however, that the social context may be changed by the introduction of the innovation itself. As disused above, diffusion may occur through existing structural patterns, or changes in such patterns may occur as a result of a technological trigger. At the same time, the individuals may reevaluate their decision of adoption with the effects of other communications. Therefore, we assumed that the IT innovation diffused through the related social network. Proposal 1: The diffusion of IT innovation will occur through the established structural pattern of social network. If the adoption occurred through the social network cohesion, the initial adopters who were more central compared with initial adopters who were less central, the prior initial adopters were more likely to be perceived as powerful effects on network structure. [25,26] Therefore, if the initial adopter s position placed more important, we assumed the more adopters would be at the end process of diffusion. Proposal 2: If the initial adopters are more central, the diffusion process will lead to more extent of IT innovation diffusion. Network density was the ratio of the actual to the maximum number of links between actors in a network. Adoption of a new technology via contagion will be more closely related to network structure after the diffusion. [6] The network density was used to measure the close extent among

9 actors through the social network, thus, we assumed that at the end of innovation diffusion, more density social network structure would occur. Proposal 3: At the end of IT innovation diffusion, the density of social network will increase. When an individual become an initial adopter of new information technology, it provided the opportunity to increase their centrality and power. [6] Thus, we assumed the initial adopters would have more centrality and powerful when the diffusion process stopped. Proposal 4: The initial adopter will be more central and powerful at the end of IT diffusion. As suggested above, if a potential adopt could reach more adopters and the adopters assessments for new information technology was very positive, it was more likely that the potential adopters decisions were impacted by the reachable adopters. Thus, we assumed that the non-adopters and adopters had a significant difference on the centrality. Proposal 5: A significant difference of centrality and power between the adopters and nonadopters will occur at the end of IT innovation diffusion. The diffusion of new information technology would overwhelm the power distribution and the social network structure, the adopters may increase their central and power while the nonadopters may lose their social network capital at the same time. Proposal 6: Some of the non-adopters lose some power and centrality at the end of IT innovation diffusion. IT innovation diffusion function Diffusion function Based on the individual intention perspective and social network perspective, we developed the IT innovation diffusion function: m B, = I + ( P / path, i k i k k i k )....(1)

10 Where Potential adopter i s bandwagon assessment of the innovation, in bandwagon cycle k Potential adopter i s intention to adopt The ability of adopter k to exert influence A comprehensive model of IT diffusion through social network By synthesizing the prior research and diffusion function, we were going to propose our IT diffusion model which was depicted by Figure 2. As illustration by Figure2, Proposal 1, 2, 3 described the effects from end-user s adoption to social network. Hypothesis 4, 5, 6 summarized the effects from social network to end-user s adoption. The diffusion process yielded the diffusion function. The process : Yield new diffusion-rate function IT innovation adoption for end-users The effects of end-user adoption on social network: P1, P2, P3 Social network 用户 The effects of social network on end-user adoption: P4, P5, P6 Figure 2 The interaction model between IT innovation adoption and social network

11 Concluding remarks In our study, we reviewed the IT innovation diffusion literatures and proposed a comprehensive model to explain the interaction effects and process between end users adoption and the related social network. The first contribution of this work was the review which summarized the current state of knowledge about IT innovation diffusion. This allowed the prospective researchers of innovation diffusion to position themselves within a cumulative tradition that had much knowledge to offer. The practical contribution of this work was the comprehensive and innovative model which synthesized the prior propositions and further exploited the diffusion function by applying social network theory and traditional individual model (TAM). Empirical study could be continuous based on our proposed model. And the implementers of IT innovation could predict the IT diffusion process and effects by using the model and thus assisted them to better implementation. In the future, the interdependent impacts among organizational and IT innovation adoption should be a prospective research topic. The structure patterns and characteristic of social network on the organizational level will be far different from individual level and in turn effect the IT innovation diffusion by a distinctive way.

12 References 1) Rogers, E.M.(1995), diffusion of innovations, 3d ed., New York: the free press. 2) Robert G.Fichman. information technology diffusion : a review of empirical research Proceedings of the Thirteenth International Conference on Information Systems, ) Brancheau, J. C.; and Wetherbe, J. C. The Adoption of Spreadsheet Software: Testing Innovation Diffusion Theory in the Context of End User Computing. Information Systems Research, Volume 1, 1990, pp ) Davis, F.; Bagozzi, R.; and Warshaw, R. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, Volume 35, 1989, pp ) Davis, F.; Bagozzi, R. and Warshaw, R. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, Volume 35, 1989, pp ) Marlene E. Burkhardt and Daniel J. Brass. Changing Patterns or Patterns of Change: The Effects of a Change in Technology on Social Network Structure and Power. ASQ,35(1990) ) Gatignon, H.; and Robertson, T. S. Technology Diffusion: An empirical Test of Competitive Effects. Journal of Marketing, Volume 53, 1989, pp ) James T.C.Teng, Varun Grover,and Wolfgang Guttler. Information technology innovation: general diffusion patterns and its relationships to innovation characteristics. IEEE Transactions on Engineering Management. Vol.49,No.1,2002,pp.13 27

13 9) Leonard Barton, D. Implementation Characteristics of Organizational Innovations. Communication Research, Volume 15, 1988, pp ) C. Haythornthwaite,M.M. Kazmer,J. Robins,S. Shoemaker. Community development among distance learners,journal of Computer Mediated Communication,2000, ) Constant, DSproull, L,Keisler, S. The kindness of strangers: The usefulness of weak ties for technical advice, OrganizationScience,1996, ) Rogers and F.F Shoemaker(1971), communication of innovations: A cross cultural approach, New York: Free Press. 13) Burt,R.S. innovation as structural interest: rethinking the impact of network position on innovation adoption Social Networks(2),1981, ) Burt,R.S. social contagion and innovation, cohesion, versus structural equivalence American Journal of sociology, 92, ) Cooper, R. B.; and Zmud, R. W. Information Technology Implementation Research: A Technological DiffusionApproach. Management Science, Volume 36, 1990, pp ) Cooper, R. B.; and Zmud, R. W. Information Technology Implementation Research: A Technological DiffusionApproach. Management Science, Volume 36, 1990, pp ) Zmud, R. W. Diffusion of Modern Software Practices: Influence of Centralization and Formalization. Management Science, Volume 28, 1982, pp ) Burns,L.R and D.R.Wholey, adoption and abandonment of matrix management programs: effects of organizational characteristics and inter organizational networks, Academy of management journal, 36,

14 19) Burt, R.S. innovation as structural interest: rethinking the impact of network position on innovation adoption, Social network,2, 1981, ) March, J.G. and J.P.Olsen, Ambiguity and Choice in organizations,bergen,norway: Universitetsforlaget. 21) Abrahamson and Rosenkopf. Institutional and competitive bandwagons, Academy of Management review,18,1993, ) Eric and Lori, Social network effects on the extent of innovation diffusion: a computer simulation, organization science,vol8(3),1997, ) Hollander,E.P. Principles and methods of social psychology, New York: Oxford University Press 24) Burt, Social contagion and innovation, cohesion, versus structural equivalence, American Journal of sociology, 92, ) Brass "Being in the right place: A structural analysis of individual influence in an organization." Administrative Science Quarterly,29; 1984, ) Tushman, Michael L, "Work characteristics and subunit communication structure: A contingency analysis." Administrative Science Quarterly,1979, 24: ) Gary C.Moore, Izak Benbasat. Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research,1991,192 22

15 Appendix: The network theory applied in IS research Classification Title of article Authors Journals Time Pages Concise summary Disciplines Relationship: Relationships among the Biehl.M. Social academic business disciplines: Kim, H. OMEGA 2006 network was a multi method citation Applied to analysis Wade, M. 371 analyze the Wade, M. relationship If the Tree of IS Knowledge Journal of among Falls in a Forest, Will Kim, H. AIS, Vol business Anyone Hear?: A Commentary No disciplines on Grover et al. Biehl, M. Structure organization: The effects of IT on organization internal structure based on social network analysis Changing patterns and patterns of change The effects of a change in technology on social network structure and power Group development (I): A review & synthesis of developmental models. Group development (II): Implications for GSS research and practice. Network Structure in Virtual Organizations Burkhardt, M.E. Brass, D.J. Chidambaram, L. Bostrom, R. P Ahuja, M. K. Carley ASQ 35(1) Group Decision & Negotiation 6 Organization Science Using large scale sociometric analysis on about 140,000 citations to show how top journals in various academic business disciplines relate to one other. Discuss the role of IS research based on the social network analysis Seek to model the effects of a change in technology on social network structure and power in organization based on the social network analysis. examines the issue of group development and its impact on the study and design of group support systems examine the relationship among task routine, organizational structure, and performance in virtual organization based on case study

16 Knowledge Management : exploring the effects of Organization network on knowledge searching transfer and accumulation Socialization in Virtual Groups Ahuja M. J. Galvin Individual Centrality and Performance in Virtual R&D Groups: An Empirical Examination Exploring the Social Ledger: Negative Relationships and Negative Asymmetry in Social Networks in Organizations Social networks and the performance of individuals and groups The Search Transfer Problem: The Role of Weak Ties in Sharing Knowledge across Organization Subunits Group Social Capital and Group Effectiveness: The Role of Informal Socializing Ties The Role of Instrumental and Expressive Social Ties in Organizational Justice Perceptions Ahuja, M. D. Galletta. K. Carley Labianca, G. Brass D.J. SPARROWE LIDEN WAYNE Morten T. Hansen Oh, Chung M H. Labianca G. Umphress, E.E Labianca, G Brass, D.J Kass, E. Scholten, L. Journal of Management Management Science Academy of Management Review Academy of Management Journal,44 ASQ 44 Academy of Management Journal, 47 Organization Science, Examines the influence of member tenure on individual communication patterns in virtual groups Social centrality is the mediator between the characteristics and the individual performance The effects of Social liability (negative relationship) on individuals performance Positive or negative network density was significantly and negatively related to group performance 1999 Findings show that weak interunit ties help a project team search for useful knowledge in other subunits but impede the transfer of complex knowledge, which tends to require a strong tie between the two parties to a transfer The effects of group social capital (positive relationship) on group performance Examine the link between different types of social ties and the interpersonal similarity of employees perceptions of interactional, procedure and distributive justice through a social network study

17 Computer Network: Explore the relationship between the computer network and the social network. The computer network form a new social network which overwhelms the traditional social network Community development among distance learners The kindness of strangers: The usefulness of weak ties for technical advice The impact of computer based communication on the social structure of an emerging scientific specialty The Open Source Software Development Phenomenon: An Analysis Based On Social Network Theory Computer Networks as Social Network: Collaborative Work, Telework, and Virtual Community C. Haythornthwaite M.M. Kazmer J. Robins S. Shoemaker Constant, D Sproull, L Keisler, S Journal of Computer Mediated Communication Organization Science L. Freeman Social Networks 6 Greg Madey Vincent Freeh Renee Tynan B. Wellman, J. Salaff, D. Dimitrova L. Garton, M. Gulia C. Haythornthwaite AMCIS 2002 Annual Review of Sociology Explored social support and community development among members of a computer supported distance learning program Explored the process of giving and receiving technical advice over an organizational computer network and suggest how this process can lead to useful advice based on weak ties and organization motivation theory. The study was to determine the impact of using computers to facilitate communication among some of the members of an emerging in science. The paper hypothesized that open source software development can be modeled as self organizing, collaboration, social networks and valid it based on network analysis. Computer supported social networks (CSSNs) are becoming important bases of virtual communities, computersupported cooperative work, and telework.