Introduction
As Ken Matejka and Julian Ramona (1993) succinctly assert, “In today’s world organizational change is inevitable… as is resistance to change”. If rapid change and resistance to change are both inevitable and accelerating, the only effective organizational solution is to proactively accept change and resistance in an effort to manage them. Personally, I would take this one step further and not only acknowledge conflict is good but it is necessary to elicit different points of view and this diversity is necessary for proper IS design. Now having said this, Information Systems (IS) can further support my viewpoint as the use of IS can support the dynamic organic structures necessary to facilitate rapid change (Robbins & Judge, 2007) and we can use of the Technology Acceptance Model (TAM) (Davis, 1986) to assess and manage IS induced stress and resistance. Central to this framework is effective communication based on trust.
Change Management
Citing the inherent multi-dimensional individual level differences of a diverse workforce further influenced by organizational culture, structural differences and unpredictable environmental pressures (Robbins & Judge, 2007), change and resistance to change is a highly complex, emotional and inevitable organizational occurrence.
Robbins and Judge (2007) state the 2 goals of effective change management are: (a) improve an organization’s agility necessary to adapt to dynamic environmental changes and (b) change and enhance employee behavior. The ability to strategically adapt to dynamic environmental conditions requires organic organizational structures and the complimentary expertise of a diverse participatory workforce (Encyclopedia Britannica, 2008, Gluckler & Schrott, 2007; Grabher, 2002, Grabher 2004; Lu, Watson-Manheim, Chudoba, & Wynn, 2006; Robbins & Judge, 2007; Watson-Manheim, Chudoba & Crowston, 2002).
Robbins and Judge (2007) cite 6 dynamic environmental forces which require continuing adaptive organizational change: (a) the nature and composition of the workforce, (b) technology, (c) emergent and evolving economies, (d) emergent and evolving competition, (e) social trends, and (f) world politics. In order to constrain the scope of this paper, I will restrict my analysis to (a) the nature of the workforce and (b) the impact of emergent and transitory Information Technology (IT) as their correlation with the 2 goals of effective organizational change management cited above and IS is obvious.
Resistance to Change
Today’s workforce must necessarily adapt to and remain abreast of emergent, evolving and transitory technology to ensure organizations can respond to change and remain agile and competitive (Association of Computing Machinery’s [ACM] Code of Ethics 1992; Robbins & Judge, 2007). Considerable research on IT adoption has shown that technology can be a source of resistance and stress (Compeau & Higgins, 1995; Compeau, Higgins, & Huff, 1999; Davis, 1986; Davis, 1989; Davis, Bagozzi, & Warshaw, 1989; Sahu & Gupta, 2007; Tsai & Su, 2007). Technology adoption may be driven from the top-down by management from the bottom-up by the individual users follow different processes and incur different impacts (Fuller, Hardin & Scott, 2007; Rogers, 1995).
Robbins and Judge (2007) state that resistance can be overt or covert, implicit or explicit, immediate or deferred. I direct the reader to O’Connor’s Resistance: The Repercussions of Change (1993) as this author has developed a matrix for identifying and managing resistance consistent with Robbins and Judge (2007) however proper discussion of this paper will far exceed our length limits.
Robbins and Judge assert that resistance can be further qualified as: positive when it provides stability, predictability and constructive functional conflict, or negative when it prevents adaptation and progress. Matejka and Romona (1993) cite 10 necessary factors to overcome resistance and facilitate successful organizational change consistent with Robbins and Judge (2007). The factors particularly relevant to our analysis culled from the intersection of Matejka and Romona (1993) and Robbins and Judge (2007) are: education, communication, participation and trust. It should be noted these components are well documented as requisite IS development best practices (i.e. Joint Application Development [JAD]) (Laudon & Laudon, 2004; Pearlson & Saunders, 2006; Satinger, Jackson, & Burd, 2002). With this basis I will further constrain my analysis to top down driven IT adoption which requires high levels of education, communication, participation and trust (Fuller, Hardin & Scott, 2007; Laudon & Laudon, 2004; Pearlson & Saunders, 2006; Satinger, Jackson, & Burd, 2002).
Technology Induced Organizational Level Change
IS are designed to improve services and facilitate efficient and effective problem solving in multiple disciplines (Laudon & Laudon, 2004; Pearlson & Saunders, 2006; Sahu & Gupta, 2007; Satinger, Jackson, & Burd, 2002; Tsai & Su, 2007). IS directly support team work through electronic communications, collaboration software and project management software (Friedman, 2005; Laudon & Laudon, 2004; Pearlson & Saunders, 2006; Satinger, Jackson, & Burd, 2002; Tsai & Su, 2007). IS are requisite support to facilitate the participatory workforce postulated in 1960 by Douglas McGregor (2005) and expanded by Rensis Likert (1967) as today’s diverse work force is increasingly virtual, team oriented and unconstrained by geographical boundaries (Robbins & Judge, 2007). Furthermore, several authors postulate that proper Information Systems implementation can break down an organization’s functional organizational barriers thereby making the organizational structure appear more process oriented (Pearlson & Saunders, 2006) which I believe is analogous to the organic structures we seek to facilitate change management.
To properly frame this analysis, it must be noted that no component in the Business-Information Technology-Organization triangle relation can be treated in isolation as they are inseparably interrelated (Pearlson & Saunders, 2006). This means a change to one component (e.g. IT) will invariably affect the other two (i.e. Business and Organization) and it would be unwise to institute a change in one component without predictive analysis of causal changes to the other two components. Furthermore, business goals must be the driving factor for change which means changes to supporting IT must be implemented solely to enhance organizational efficiency and effectiveness (Gartner Research, 2007; Pearlson & Saunders, 2006).
Roche Pharmaceuticals
To illustrate this business-IT-organization interdependence, I provide an example where a change to supporting IT results in individual resistance in the organizational culture (OC) component thereby negatively affecting organizational productivity. This example comes from the dynamic Pharmaceuticals industry and is summarized from Pearlson and Saunders (2006) analysis of G. Anders – Fresh Start 2002: Roche’s New Scientific Method (2002).
Swiss pharmaceutical giant, Roche Group’s business model was based on veteran scientific teams competing against one another for resources and rewards. This highly successful model was responsible for extraordinary pharmaceutical advances however it also created a powerful competitive and insular culture impeding cross team communication which would benefit the organization as a whole. As a result, researchers hoarded their technical expertise since sharing knowledge would allow other teams to catch up. Additional analysis revealed teams also exhibited “escalation of commitment” (Robbins & Judge, 2007) as they rarely abandoned faltering projects since they had invested substantial time and resources to their projects.
During the late 1990’s Roche developed the computer based GeneChip to facilitate and speed up experiments in pharmaceutical research. The GeneChip simulated the human genome and allowed scientists to observe a gene’s reaction to various chemicals. This extraordinary breakthrough allowed researchers to rapidly identify and isolate diseases at the gene level and test treatments at a speed that had been unimaginable a few years ago. Researchers were able to identify individual toxicity risks and tailor drugs to specific people thereby launching the era of customized medicines. Unfortunately, this IT breakthrough had unforeseen ramifications as Roche did not proactively address the necessary corollary changes to its business model or OC.
From an IT perspective, consider that each initial sample run on a GeneChip set generates 60 million bytes of raw data and further analysis of the data requires an additional 180 million bytes of computer storage. Roche researchers ran over 1,000 GeneChip experiments the first year which decreased the GeneChip’s availability and came close to collapsing their data systems. Further analysis revealed 10 people were hogging 90% of the company’s total computer capacity. It became apparent the existing competitive OC had researchers running experiments 24 hours a day in an effort to mark their territory thereby preventing other researchers from undertaking critically important research. As a result, despite the advance in technology the company was not experiencing increased productivity.
In response to this failure, Roche sought to replace their internally competitive mentality with a more collaborative style based on teamwork. Roche was unsuccessful in changing the organizational behavior of their existing employees thus they changed their Human Resources hiring practice by recruiting more collaborative researchers. Additionally, Roche addressed its growing “escalation of commitment” problem by implementing “Fail Fast”. Fail Fast imposed strict time limits ensuring research was always directed towards bright prospects rather than repeatedly pursuing less than optimal alternatives and resulted in increased efficiency. (p. 34 – 37)
I believe the GeneChip provided a clear competitive advantage which warranted its development and top down adoption. From the available literature it is impossible to ascertain if a proper analysis of all elements (i.e. business, IT and organization) using JAD was conducted. Roche scientists openly embraced the introduction of new technology however they did not adjust their organizational culture and it is unclear if the individual recognition and rewards system was modified to promote the requisite cultural change. This echoes support for the Pearlson and Saunders (2006) assertion that a change to any component of the business-IT-organization triangle requires comprehensive analysis prior to implementation. This case study also supports Robbins and Judge (2007) assertion that companies with strong cultures excel at incremental change but encounter substantial resistance when faced with radical change. Additionally, Roche’s inability to change its existing employee attitudes is in concert with the Robbins and Judge (2007) assertion that successful companies and their employees may resist change when past practices have been proven successful.
Further analysis revealed that Roche had to change the nature of its workforce (Robbins & Judge, 2007) through its hiring procedures by seeking more collaborative open minded individuals (Pearlson & Saunders, 2006). This was necessary to accommodate their new business model, emergent technology and evolved corporate culture and is in concert with Socio-technical theory (Encyclopedia Britannica, 2008) which advocates using OB empirical results to hire employees who accept change focusing on the Big 5 personality trait – openness to experience (Robbins & Judge, 2007).
Technology Induced Individual Level Resistance
To discuss individual level technology induced stress and resistance I suggest the use of the Technology Acceptance Model (TAM) developed by Fred Davis (1986) as it has gained wide acceptance and proven useful (Sahu & Gupta, 2007). Davis’ seminal work sought to determine the causal factors promoting or impeding technology adoption. Davis identified the independent variables “Perceived Usefulness” (PU) and “Perceived Ease of Use” (PEU) and found a correlation with an individual’s “Intent to Use” (ITU) or adopt new technology. Davis defined PEU as the subjective probability a user expects the system to be free of effort therefore requiring minimal training and (PU) as the subjective probability IT will increase job performance (Sahu & Gupta, 2007). Subsequent research found PU has a significantly stronger positive correlation with ITU (Davis, 1989; Sahu & Gupta).
Subsequent research has added significantly to Davis’ work. Particularly relevant to this analysis of stress and resistance is the addition of computer self efficacy (CSE) to the TAM (Compeau & Higgins 1995; Compeau, Higgins, & Huff, 1999; Davis, Bagozzi, & Warshaw, 1989). CSE is a measure of an individual’s subjective perception of their ability to use system on their own and is adapted from Bandura’s (1896) social cognitive theory (Compeau & Higgins 1995; Compeau, Higgins, & Huff, 1999; Davis, Bagozzi, & Warshaw, 1989). These authors cite CSE affects a user’s persistence to use a system and their resultant emotional responses (e.g. stress and anxiety). It has been shown that a strong sense of computer self-efficacy will allow individuals to persist and overcome difficult problems and conversely individuals with low CSE give up easily and experience stress (Compeau & Higgins, 1995). Additional research has found an individual’s CSE to be directly related to and individual’s PEU (Gong, Xu & Yu, 2004) further solidifying this model’s use. Researchers have also identified a social influence to technology adoption defined as the degree an individual perceives others feel technology is important (Thompson, Higgins, & Howell, 1991).
The commonality between IS JAD and the TAM it is obvious they both require high levels of communication, education, training and trust (Compeau & Higgins 1995; Compeau, Higgins, & Huff, 1999; Davis, Bagozzi, & Warshaw, 1989; Laudon & Laudon, 2004; Pearlson & Saunders, 2006; Satinger, Jackson, & Burd, 2002). It is well know that proper IS design and development should use JAD and an iterative development process whereby all participatory constituencies remain in constant communications (Laudon & Laudon, 2004; Pearlson & Saunders, 2006; Satinger, Jackson, & Burd, 2002). The continual communications must be comprehensive and detailed and rely on implicit trust between all parties (Matejka & Ramona, 1993; Robbins & Judge, 2007).
Summary: Recommended Approaches to Managing Change and Stress.
Robbins and Judge (2007) state the thrust of OB research is to define and measure the independent variables and their relation to dependant variables thereby facilitating predictive analysis of behavior and optimization of organizational efficiency and effectiveness. I strongly advocate the use of Technology Acceptance Model (Davis, 1986) and proper Joint Application Development (JAD) when introducing new Information Technology (Laudon & Laudon, 2004; Pearlson & Saunders, 2006) as this will provide OB with a predictive assessment tool and a management methodology. I also assert that organizations embrace resistance as it is fundamental to interactionist conflict and can lead to positive outcomes (Robbins & Judge, 2007). Anecdotally, I have professionally observed that management’s resistance to top down driven, IT induced employee resistance fuels the conflict further thereby impeding its resolution. I assert the solution is to use JAD, education and training and maintain high levels of communication based on trust throughout the process.
I strongly assert that organizations do not adopt technologies for technology’s sake as we have established the introduction of new technology can induce individual level stress, anxiety and resistance (Compeau & Higgins 1995; Compeau, Higgins, & Huff, 1999; Davis, Bagozzi, & Warshaw, 1989) and result in unpredictable organizational consequences (Gartner Research, 2007; Pearlson & Saunders, 2005). Technology must be introduced only when it serves and supports a business or organizational need. We should remember that IT is itself a business and thus seeks to stimulate the adoption of emerging technologies to increase its business (Gartner Research, 2007).
In summary, Ken Matejka and Julian Ramona (1993) cite “standing still is a great way to be passed up by the competition”. We have established that change is inevitable and that organizations must necessary change to remain competitive. I argue that effective communication and the application of OB empirical research and IT best practices are the key to achieving successful: change management, stress management, IT adoption, organic work structures and organizational success.
References
Anders, G. (2002). Fresh start 2002: Roche’s new scientific method. Fast Company (January 2002), available at http://www.fastcompany.com/online/54/roche.html
Association of Computing Machinery (ACM). (1992). ACM code of ethics and professional conduct. Retrieved March 7, 2008 from http://www.acm.org/about/code-of-ethics.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
Cetron, M. J., & Davies, O. (2008). Trends shaping tomorrow’s world: Forecasts and implications for business, government, and consumers (Part One). The Futurist, 42(2), 35-52.
Compeau, D. R., & C. A. Higgins. (1995). Computer self-efficacy: development of a measure and initial test. MIS Quarterly 19(2): 189-211.
Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145-158.
Davis F. D., (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral dissertation, MIT Sloan School of Management, Cambridge, MA.
Davis, F.D., (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, P. R., & Warshaw, P. R., (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 892-1003.
Friedman, T. L. (2005). The world is flat: A brief history of the 21st century. NY:Farrar, Straus and Giroux
Fuller, M. A., Hardin, A. M., & Scott, C. L., (2007). Diffusion of virtual innovation. Database for Advances in Information Systems, 38(4), 40-44.
Gartner Research Inc., (2007). Gartner says IT leaders should use a business model for justifying emerging technologies. Anonymous Business Wire New York: Oct 11, 2007. Retrieved March 21, 2008, from Business Dateline database. (Document ID: 1362988561).
Glückler, J., & Schrott, G., (2007). Leadership and performance in virtual teams: Exploring brokerage in electronic communication. International Journal of E-Collaboration, 3(3), 31-52.
Grabher, G. (2002). The project ecology of advertising: Talents, tasks, and teams. Regional Studies, 36, 245-62.
Grabher, G. (2004). Learning in projects, remembering in networks? Communality, sociality, and connectivity in project ecologies. European Urban and Regional Studies, 11, 103-23
Industrial relations. (2008). In Encyclopedia Britannica. Retrieved March 24, 2008, from Encyclopedia Britannica Online: http://search.eb.com/eb/article-66853
Laudon, K. C., Laudon, J. P. (2004). Management information systems (8th ed.). Upper Saddle River, NY: Pearson Publishing.
Likert, R. (1967) The human organization. Mc-Graw Hill: New York, New York.
Lu, M., Watson-Manheim, M. B., Chudoba, K. M., & Wynn, E., (2006). Virtuality and team performance: Understanding the impact of variety of practices. Journal of Global Information Technology Management, 9(1), 4-23.
Matejka, K. & Ramona, J. (1993). Resistance to change is natural. Supervisory Management, 38(10), 10
McGregor, D., (2005). The human side of enterprise, annotated edition, McGraw Hill: New York, New York.
Tsai, M. T., & Su, W., (2007). The impact of cognitive fit and consensus on acceptance of collaborative information systems. The Business Review, Cambridge, 8(2), 184-190.
O Connor, C. A., (1993). Resistance: The repercussions of change. Leadership & Organization Development Journal, 14(6),
Pearlson, K. E., & Saunders, C. S. (2006). Managing and using information systems (3rd ed.). Hoboken, NY: Wiley Publishing.
Robbins, S. P., & Judge, T. A., (2007). Organizational behavior (12th ed.). Upper Saddle River, NJ: Prentice Hall
Rogers, E.M., (1995). Diffusion of innovations. New York: Free Press.
Sahu, G. P., & Gupta, M. P., (2007). Users’ acceptance of e-government: A study of indian central excise. International Journal of Electronic Government Research, 3(3), 1-21.
Satinger, J. W., Jackson, R. B., & Burd, S. D., (2002). Systems analysis and design (2nd ed.) Boston, MA: Course Technology
Thompson, R. L., Higgins, C. A., & Howell, J. M., (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 124-143.
Watson-Manheim, M.B., Chudoba, K., & Crowston, K. (2002). Discontinuities and continuities: A new way to understand virtual work. Information, Technology and People, 15(3), 191-209.