The Influence of a Convergence in Understanding Between Technology Providers and Users on Information Technology Innovativeness

The objective of this research was to determine if a convergence in understanding between providers and users of a technology would result in greater innovativeness regarding that technology. Two mechanisms were proposed for achieving greater convergence: (1) more frequent communication and (2) the use of richer communication channels. Here, convergence represents the degree of mutual understanding between the technology providers and the other business personnel about the firm's business activities and the importance of the technology in supporting those activities.Frequency of communication indicated the degree to which the technology providers and the business personnel had communication contact, while richness of communication was determined by the type of communication channel used. These means of communication ranged from face to face, computer mediated, to written channels of communication. The convergence construct was operationalized in terms of the value chain framework where 14 business activities (primary and secondary to the value chain) were identified. Convergence thus represents the degree of mutual understanding between the technology providers and the business personnel regarding the importance of these business activities and the importance of the technology in supporting these activities. Innovativeness was determined through expert evaluation of information technology innovativeness.This research was conducted in two United States divisions of a large multinational firm. The units of analyses for the research constructs were the departments in these two divisions. The constructs were measured over five periods of data collection so that longitudinal, causal analysis techniques (cross-lagged correlations and path analysis) were used to investigate the research model.The following results were obtained: (1) convergence was found to be a predictor of innovativeness, (2) communication richness was a predictor of convergence, and (3) communication frequency was a predictor of both convergence and communication richness. This study provided two important extensions to the often-studied relationship between communication behaviors and innovativeness. First, this research showed empirically that the richness of communication influences innovativeness and, in fact, may be the more relevant predictor variable. Secondly, this research showed that convergence is an important intervening construct in the communication activity/innovativeness relationship. Interestingly, the research model only suggested a causal relationship for convergence on the importance of the primary business activities. Thus, the intent of this study to examine the proposition that frequent and rich communication exchanges produce a convergence in understanding among technology providers and users and, that this convergence directly promotes organizational innovativeness was supported.

[1]  J. Elashoff,et al.  Multiple Regression in Behavioral Research. , 1975 .

[2]  J. Ettlie Manpower Flows and the Innovation Process , 1980 .

[3]  Michael X Cohen,et al.  A Garbage Can Model of Organizational Choice. , 1972 .

[4]  Roger K. Blashfield,et al.  Performance of a composite as a function of the number of judges , 1978 .

[5]  D. Rousseau Issues of level in organizational research: Multi-level and cross-level perspectives. , 1985 .

[6]  E. Rogers,et al.  Communication of Innovations; A Cross-Cultural Approach. , 1974 .

[7]  R. Billings,et al.  Use of path analysis in industrial/organizational psychology: Criticisms and suggestions. , 1978 .

[8]  R. Daft A Dual-Core Model of Organizational Innovation , 1978 .

[9]  Lawrence B. Mohr,et al.  Conceptual issues in the study of innovation , 1976 .

[10]  Richard L. Daft,et al.  Message Equivocality, Media Selection, and Manager Performance: Implications for Information Systems , 1987, MIS Q..

[11]  Roy Rothwell,et al.  The role of communications in technological innovation , 1973 .

[12]  J. Pfeffer Management as symbolic action: the creation and maintenance of organizational paradigms , 1981 .

[13]  L. Smircich,et al.  Leadership: The Management of Meaning , 1982, The Journal of applied behavioral science.

[14]  Todd J. Hostager,et al.  Jazz as a Process of Organizational Innovation , 1988 .

[15]  Richard L. Daft,et al.  Organizational information requirements, media richness and structural design , 1986 .

[16]  J. Coleman,et al.  Medical Innovation: A Diffusion Study. , 1967 .

[17]  D. Alfredo Social Structure and the Diffusion of Innovation , 1968 .

[18]  L. Tucker,et al.  Procrustes matching by congruence coefficients , 1976 .

[19]  M. Tushman Special Boundary Roles in the Innovation Process. , 1977 .

[20]  A Significance Test For Congruence Coefficients For Cattell's Factors Matched By Scanning. , 1978, Multivariate behavioral research.

[21]  M. Porter,et al.  How Information Gives You Competitive Advantage , 1985 .

[22]  T. Cook,et al.  Quasi-experimentation: Design & analysis issues for field settings , 1979 .

[23]  G. Zaltman,et al.  Innovations and organizations , 2020, Organizational Innovation.

[24]  M R Louis,et al.  Surprise and sense making: what newcomers experience in entering unfamiliar organizational settings. , 1980, Administrative science quarterly.

[25]  Ronald S. Burt,et al.  Differential Effects of Information Channels in the Process of Innovation Diffusion , 1975 .

[26]  A. V. D. Ven,et al.  Central problems in the management of innovation , 1986 .

[27]  R. Duncan Organizations and Their Environments. , 1974 .

[28]  R. Daft,et al.  Information Richness. A New Approach to Managerial Behavior and Organization Design , 1983 .

[29]  Denis M. S. Lee,et al.  Technology Transfer as a Function of Position in the Spectrum from Research Through Development to Technical Services , 1979 .

[30]  E. Hippel The dominant role of users in the scientific instrument innovation process , 1993 .

[31]  C. Coughenour The Rate of Technological Diffusion Among Locality Groups , 1964, American Journal of Sociology.

[32]  James M. Utterback,et al.  The Effects of Communication on Technological Innovation , 1984 .

[33]  Louis W. Stern,et al.  The Effect of Sociometric Location on the Adoption of an Innovation within a University Faculty. , 1976 .

[34]  Albert H. Rubenstein,et al.  The effects of perceived needs and means on the generation of ideas for industrial research and development projects , 1967 .

[35]  Robert W. Zmud,et al.  An Attribute Space for Organizational Communication Channels , 1990, Inf. Syst. Res..

[36]  K. Weick The social psychology of organizing , 1969 .

[37]  Christopher A. Higgins,et al.  The accuracy and biases of diary communication data , 1985 .

[38]  Jack M. McLeod,et al.  Interpersonal Approaches to Communication Research , 1973 .