How to Make IT Work:

How to Make IT Work:

European Management Journal Vol. 24, Nos. 2–3, pp. 199–205, 2006 Ó 2006 Elsevier Ltd. All rights reserved. 0263-2373 $32.00 doi:10.1016/j.emj.2006.03...

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European Management Journal Vol. 24, Nos. 2–3, pp. 199–205, 2006 Ó 2006 Elsevier Ltd. All rights reserved. 0263-2373 $32.00


How to Make IT Work: Cognitive Perspectives for Better Information Technologies Performance ORNIT RAZ, MIT Sloan School of Management ALBERT GOLDBERG, Technion – Israel Institute of Technology The successful implementation of new information technologies depends on the beliefs and expectations of both managers and workers. Consequently, a new information technology may enable higher productivity in one social setting while limiting or even reducing productivity within another milieu. We present a new concept called ‘‘Cognitive Knowledge Identity’’ (CKI) that distinguishes between three types of systems in organizations: Tayloristic, Expert, and Innovation. This cognitive aspect of organizational culture operates as a basic social system framework that governs the effective use of IT in organizations. This paper presents practical aspects for managers that lead for better Information Technology performance. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Information technology, Cognitive knowledge identity, Organizational culture, IT performance, Knowledge systems

Introduction and Conceptual Framework Organizational culture has been shown to influence the successful implementation of information technologies (Allen & Hauptman, 1990; Orlikowski, 2000; Huang et al., 2003; Zammuto & O’Connor, 1992). An important aspect of every organizational culture is assumptions and beliefs that define organizational identity and that set expectations and cogni-

tion about organizational objects and interactions (Albert & Whetten, 1985; Dutton & Dukerich, 1991; Jones et al., 2005). These include beliefs about roles, relationships, goals and interactions with new technology (Griffith, 1998). Cognitive systems control organizational behavior by dominating perceptions about ‘‘what the world is’’ – what kind of actions can be taken, and by whom (Scott & Christensen, 1995) – and ‘‘Who are we?’’ (Albert & Whetten, 1985). The answers to these questions influence the definition and resolution of organizational issues (see Dutton & Dukerich, 1991; Dutton, Dukerich & Harquail, 1994). We see the cognitive element as a primary factor in the creation of categories that describe organizational routines. In our study we focus on the way that ‘‘knowledge’’ itself is perceived by the organization. We suggest that a combination of cognitive dimensions produces three unique cognitive knowledge identities (CKIs). First, Tayloristic identities (t) are based on an externalization of knowledge, where workers follow rules and procedures without understanding the rationale behind them. Second, Expert identities (e) are built on worker competencies. Finally, Innovation identities (i) call for workers to engage in a creative act, conceiving and developing new knowledge. The three identities can be further described as follows:

European Management Journal Vol. 24, Nos. 2–3, pp. 199–205, April–June 2006



(1) Tayloristic This approach has become central to many organizations. In a Tayloristic knowledge system, organizations seek to make knowledge explicit by coding it into work procedures or embedding it in technologies (Cowan & Foray, 1997; Cowan et al., 2000). Workers are not expected to understand what they are doing as long as they follow rules and regulations under the strict supervision of direct managers. Workers are motivated to fulfill a contract with their employer in which they receive agreed-upon remuneration in return for disciplined work. (2) Expert In contrast to the Tayloristic system, the Expert knowledge system requires workers to make decisions regarding the use of knowledge. Knowledge is personalized as workers make use of their own ‘‘tacit knowledge’’ that includes know-how and skills relevant to their work (Hansen, Nohria & Tierney, 1999). Workers are expected to be self-motivated to complete their tasks successfully. (3) Innovation This knowledge system involves workers in the development of new knowledge. Workers are motivated by a work-related challenge and make use of their intuition and insight in the quest for creative solutions. The work environment allows workers to exercise freedom in the exploration of new ideas with colleagues both within their organization and in the external scientific community (Majchrzak, Cooper & Neece, 2004).

As noted, we believe that cognitive knowledge identities (CKIs) set the framework for interactions between employees and their organizations which affect IT performance. We argue that optimum performance occurs when there is a match among three elements: that is, when an organization’s CKI matches both the IT design (see for example Gobbin, 1999) and the task orientation (see Figure 1). Such a match will arise in one of three situations: When the dominant CKI is ‘‘Tayloristic’’ (t), the dominant IT design is ‘‘rigid’’ (r), and the dominant task objective is ‘‘production’’ (p) when the dominant CKI is ‘‘Expert’’ (e), the dominant IT design is ‘‘elastic’’ (el) and the dominant task objective is specialized’’ (s); or when the dominant CKI is ‘‘Innovation’’ (i), the dominant IT design is ‘‘dynamic’’ (d) and the dominant task objective is ‘‘R & D’’ (r).


b. Sample

Given that one particular cognitive knowledge identity may be dominant in an organization, organizations are likely to take a contingency approach to the introduction of information technologies (Newell et al., 2002). Successful adoption of an information technology in an organization with a Tayloristic identity calls for an embedded structure that enables greater control by management. Where possible, such technologies are also directed at replacing human beings. An Expert identity calls for information technologies that provide essential information and allow for increased knowledge exchange with colleagues. An Innovation identity should develop information technologies that provide access to knowledge and opportunities for testing new concepts.

Data were collected from 580 employees at 18 organizations. Firms varied from high-tech industrial enterprises with extensive R & D operations to service organizations such as banks and hotels.

Some information technologies may be primarily directed at increasing management control over task execution, while others facilitate the work of professionals by providing essential information, or by encouraging greater cooperation among specialists. As a consequence, a failure to successfully introduce various information technologies can be explained by a mismatch between an organizational culture and the underlying structure of a new information technology (Newell, Scarbrough, & Swan, 2001). 200

Method a. Construction of Research Instruments A review was made of the research literature on cognitions related to workers and the use of knowledge at work. Interviews were conducted with managers and workers. On the basis of these findings, a questionnaire was constructed, pre-tested in two organizations with 136 workers and managers, and then substantially improved in order to increase the reliability and validity of questions to be used in the field stage of the research.

c. Measurement of Variables 1. Cognitive Knowledge Identities Items for the cognitive knowledge identities (CKIs) included references to both the ‘‘signification aspect’’ (i.e., the nature of the worker) and to ‘‘interpretation aspects’’ (linkages between employees and their firms, managers, colleagues, and external environment). Workers were asked to express their agreement or disagreement with 32 different cognitive evaluations of their organizations. The items were scaled along a Likert Scale with five categories, from 1 = ‘‘strongly agree’’ to 5 = ‘‘strongly disagree’’. The factor analysis revealed three key factors as independent dimensions for cognitive knowledge identities. Table 1 shows 14 items that scored high in a factor analysis. These items were used in the construction of indices to measure the three cognitive

European Management Journal Vol. 24, Nos. 2–3, pp. 199–205, April–June 2006


Figure 1 Match Between CKI, IT Design & Task Leads to Better Performance: An Ideal Type  A, B & C illustrate Three Different Situations that Lead to Better IT Performance

identities: the ‘‘Tayloristic identity’’ (Standardized Alpha = .71), ‘‘Expert identity’’ (Standardized Alpha = .68) and ‘‘Innovation identity’’ (Standardized Alpha = .73). 2. Information Technology Design IT system described via the following 3 factors: Rigid, Elastic and Dynamic. Each factor reflects a different IT design (structure) while the scale is ordinal: Rigid – Information systems that control and guide employee behavior. These systems are common at the operational or tactical levels of leading IT applications such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) and Transaction Processing systems (TP). Elastic – Information systems that support worker’s expertise. Such systems are often used in the context of diagnostic tasks in fields such as medicine, engineering and continuous-process production. In such

cases Decision Support Systems (DSS) help experts to diagnose problems and respond to events. Dynamic – Information systems that support worker innovation. Dynamic systems are used in the context of Strategic Management Systems, R & D and Marketing. Business Intelligence (BI) systems offer features such as Online Analytic Processing (OLAP), Data Mining and Content Mapping to allow workers to explore vast amounts of information in order to build intuition and foster the emergence of creative ideas. In addition, collaborative features such as Moderated Discussion Forums are common in creative fields such as Product Life Cycle Management (PLM) and help to create new ideas by streamlining the communications process that drives innovation. 3. Task Objectives Organizational tasks were categorized into three groups according to their dominant task objectives:

European Management Journal Vol. 24, Nos. 2–3, pp. 199–205, April–June 2006



Table 1

Employee Cognitive Interpretations of their Organization

(N = 580) Tayloristic Identity Workers are doing a good job just when following strictly the rules and regulations Managers are expected to strictly control the work of their employees Workers are seen as easily replaced by new workers Workers are seen as working for a salary without any interest in the work itself Managers are expected to immediately reprimand workers who make a mistake Expert Identity Workers are seen as able to understand how to do their work and not just to follow instructions Workers are expected to try to use their personal knowledge/experience to solve work problems and not wait for new instructions from management Workers are seen as motivated to seek new knowledge wherever they can find it Workers are seen as motivated to seek a way to reach work objectives when regular methods don’t work Management is expected to encourage workers to try out their ideas even if they don’t work Innovation Identity Workers are expected to work with colleagues on new products/ideas Workers are expected to collect information from outside the organization that can be used to perform a job Workers are expected to seek and to obtain data (facts, figures) about a task or client needed to complete a job Workers are expected to test new ideas by communicating to relevant others outside the organization

(1) production/service, (2) specialized, and (3) R & D. Each job responsibility was presumed a have a different relationship to the use of knowledge: production/service workers were expected to follow procedures; specialized workers were required to have a basic understanding of the nature of their work; and R & D workers were called upon to have special abilities necessary for the creation of new knowledge. 4. Fit Fit is defined as the degree of agreement (correlation) between CKI, IT design, and Task objective. Each of these variables is represented by one value that rises continuously from minimum to maximum. The variables are appropriately scaled so that the distance between the different levels in Figure 1, e.g. between ‘rigid’ and ‘elastic’ and between ‘production’ and ‘specialized’, are equal. Fit is defined as the sum of absolute value (abs) of the differences among those variables. Using the absolute value is important since the differences might be negative, and cancel each other in ordinary summation. Hence, the variable ‘‘Fit’’ is given as: f ¼ absðCKI  ITdÞ þ absðCKI  TASKÞ þ absðIT  TASKÞ

The geometric interpretation of the fit function, as seen from Figure 1, is that it gives a measure of the unevenness of the shape created by the three points. When the match is perfect, i.e. when the score is the same in all categories, the result will be ‘‘0’’. This is 202

Factor Weight 0.65 0.50 0.43 0.66 0.58

% Agree 67 52 30 20 12





0.68 0.46

72 67



0.68 0.52

63 54





the match the organization should aspire to in order to get high IT performances. Larger values of f indicate deviation from that ideal situation. Therefore, Optimal IT performance () minimum ffg 5. Performance Workers were asked to describe the impact on their work of information technologies introduced over the previous three years. Items were based on a semantic differential scale and referred to three aspects of work performance: reductions in the time needed to accomplish a task; reductions in the cost of the task; and improvements in quality. 6. Satisfaction Workers were asked to report about the satisfaction that they receive from their work – ie, the degree to which their needs and expectations are fulfilled (Herzberg, 1959, 1980, 1991; Frøkjær et al., 2000). 7. Position Workers were asked to report the number of employees they supervise. 8. Professional Experience Length of work experience is often used as proxy for domain knowledge and technical capability (Faraj and Sproull, 2000). We asked respondents to report

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their total number of years of experience in their field.

Table 3 Regression Equations Showing the Influence of Various Factors for Work Performance— Information Technology Meets a Higher Standard of Quality Model 1

Results Table 2 presents means, standard deviations, and intercorrelation of the measured variables. There appear to be no problems of multicolinearity, since the highest correlation is .71 (between Seniority and Work experience). As shown in Model 1 in Table 3, Satisfaction, Position and Education all had a positive effect on IT quality. Also, Model 2 shows that when Fit is taken into account, the impact of these variables is slightly strengthened, and Fit itself is positively associated with IT quality, suggesting a strengthening effect of Fit (significant DR2). This model accounted for approximately 12% of the variance in IT quality. As shown in Model 1 in Table 4, Seniority had a negative effect on reductions in work time. Also, Model 2 shows that when Fit is taken into account, the impact of seniority is slightly strengthened and becomes more significant (.15* in model 1 and 18** in model 2), and Fit itself is positively associated with IT-related reductions in work time, suggesting a strengthening effect of Fit (significant DR2). This model accounts for approximately 8% of the variance in IT-related reductions in work time. As shown in Model 1 in Table 5, Satisfaction had a positive effect on IT-related reductions in work costs. Also, Model 2 shows that when Fit is taken into account, the impact of Satisfaction remains the same, while Professional experience, which had no effect in model 1, becomes positively significant (.09 in model 1 and .14* in model 2), and Fit itself is positively associated with IT-related reductions in cost, suggesting both a strengthening (in regard to Satisfaction) and mediating (in regard to Professional experience) effect of Fit (significant DR2). This model Table 2

8 9 10 11





Satisfaction Position Professional experience Seniority Gender Age Education Fit

0.03 0.05 0.01 0.00 0.05 0.00 0.04 –

0.15** 0.19** 0.13* 0.03 0.03 0.05 0.09* –

0.03 0.04 0.01 0.01 0.04 0.00 0.04 0.08

0.16** 0.18** 0.12+ 0.08 0.03 0.02 0.10* 0.11**

F(t) Constant R Squared Adjusted R Squared

8.13** 1.48** 10% 9%

7.71** 1.37** 13% 12%


P < 0.1; *P < 0.05; **P < 0.01.

Table 4 Regression Equations Showing the Influence of Various Factors on Work Performance— Information Technology Reduces Work Time Model 1

Satisfaction Position Professional Experience Seniority Gender Age Education Fit F(t) Constant 2 R Squared Adjusted R Squared

Model 2





0.00 0.01 0.00

0.00 0.08+ 0.00

0.00 0.01 0.00

0.00 0.09 0.02

0.01 0.02 0.00 0.05 –

0.15* 0.02 0.00 1.39** –

0.02 0.03 0.00 0.04 0.11

0.18** 0.03 0.01 0.12 0.18**

5.09** 2.42** 6% 5%

6.83** 2.32** 10% 8%


P < 0.1; *P < 0.05; **P < 0.01.

Means, Standard Deviations, and Correlation of the Measured Variables M

1 2 3 4 5 6 7

Model 2













Quality(1) 3.19 1.07 – Time(2) 2.90 0.91 0.02 – Cost(3) 1.85 0.93 0.02 0.00 – 0.01 0.10* – Satisfaction 2.61 1.47 0.18** Position 6.25 3.88 0.18** 0.06 0.08 0.26** – 0.12** 0.08* 0.06 0.09* – Work experience 8.87 8.58 0.15** 0.04 0.14** 0.08* 0.71** – Seniority 8.54 8.03 0.11** 0.17** (in organization) Gender – – 0.00 0.00 0.08 0.06 0.03 0.02 0.04 – 0.09 0.04 0.15** 0.01 0.63** 0.61** 0.01 – Age 35.24 11.45 0.13** Years of education 14.71 2.52 0.04 0.17** 0.06 0.00 0.10 0.19** 0.13** 0.18** 0.00 0.18** 0.04 0.04 0.13** 0.09* 0.14** 0.04 0.06 0.01 – Fit 1.68 1.48 0.10*


P < 0.1; *P < 0.05; **P < 0.01.

European Management Journal Vol. 24, Nos. 2–3, pp. 199–205, April–June 2006



Table 5 Regression Equations Showing the Influence of Various Factors on Work Performance— Information Technology Reduces Costs Model 1 B


Satisfaction 0.01 Position 0.01 Professional Experience 0.01 Seniority 0.00 Gender 0.07 Age 0.00 Education 0.02 Fit – F(t) Constant R Squared Adjusted R Squared *P

< 0.05;


Model 2

3.27** 1.60** 4% 2%



0.01 0.10* 0.05 0.01 0.09 0.01 0.02 0.00 0.06 0.07 0.06 0.00 0.06 0.01 – 0.05

0.10* 0.06 0.14* 0.01 0.06 0.01 0.04 0.09*

3.39** 1.45** 5% 4%

< 0.001.

accounts for approximately 4% of the variance in ITrelated reductions in cost.

Discussion This study continues a line of research that seeks to understand how an organization’s culture or modes of cognition can impair or improve its ability to implement new technologies (see for example Zammuto & O’Connor, 1992; Griffith, 1998; Zhong & Majchrzak, 2004; Gobbin, 1999), and the nature of the interaction between new technology and organizational culture (see for example Orlikowski, 1992). Cognitive Knowledge Identities define the way organizations understand or perceive knowledge. CKIs are important for the organization of work. They determine how workers are expected to deal with knowledge and set the framework for transactions between employees and their organizations. Three unique CKIs can be found in organizations: Tayloristic, Expert, and Innovation. Tayloristic identities are based on an externalization of knowledge, where workers follow rules and procedures without understanding the rationale behind them. Expert identities are built on worker competencies. Innovation identities call for the creation and development of new knowledge. Our models show improved organizational performance results when there is a fit between IT design, CKI and the task objectives. We distinguish between three types of IT design or structure. One type of IT is designed to control workers, and is referred to here as a ‘‘rigid system.’’ The second type supports workers’ expertise – we call this an ‘‘elastic system.’’ Finally, there is IT that supports workers’ innovative ideas – a system referred to here as ‘‘dynamic.’’ 204

We measured the effects of IT on work performance issues such as reducing the cost per task, reducing the time required per task, and meeting a higher standard of quality. In all three cases, our conjecture was that higher ‘‘Fit’’ would be positively associated with IT performance. We found strong support for our assumption. The probability that a firm will exhibit better performance when introducing a new IT is indeed significantly increased when we take fit into account.

Implication for Managers This study suggests that the performance of IT systems depends on the match between three factors: CKI, IT design and the nature of the task objectives. Such a match exists under three conditions: (1) The CKI is codified (Tayloristic identity), the IT system controls workers’ actions and strengthens supervision over workers (a rigid system), and the task objective is production/service. (2) Workers make decisions regarding the use of knowledge (Expert identity), the IT system supports workers’ expertise (an elastic system), and workers have a basic understanding of the nature of their work (specialized task objective). (3) Workers are motivated by a work challenge and make use of their intuition (Innovation identity), the IT supports workers’ innovative ideas (a dynamic system), and workers have special abilities for the creation of new knowledge (R & D task objective). Hence, managers, before introducing IT systems into their organization, have to assess this match. We argue that managers are likely to enhance the performance of new IT systems or improve old ones if they take the fit between these factors into account. The sample size examined in this research means that more work is needed to see how exactly an optimal match is necessarily tied to IT performance. Still, we believe that this study can contribute significantly to the success of IT in organizations, and more than that, to our theoretical understanding of complex organizational work environments (Orlikowski, 2002).

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Newell, S., Scarbrough, H. and Swan, J. (2001) From global knowledge management to internal electronic fences: Contradictory outcomes of intranet development. Brit. J. Management 12(2), 97–111. Newell, S., Robertson, M., Scarbrough, H., Swan, J.A., 2002. Managing Knowledge Work. Palgrave, Basingstoke. Orlikowski, W.J. (1992) The duality of technology: Rethinking the concept of technology in organizations. Org. Sci. 3(3), 398–427. Orlikowski, W.J. (2000) Using technology and constituting structures: A practice lens for studying technology in organizations. Org. Sci. 11(4), 404–428. Orlikowski, W.J. (2002) Knowing in practice: enacting a collective capability in distributed organizing. Organization Science 13(3), 249–273. Scott, W.R. and Christensen, S. (1995) Conclusion: Crafting a wider lens. In The Institutional Construction of Organizations, (eds) W.R. Scott and S. Christensen, pp. 302–314. Sage Publications, Thousand Oaks, CA. Zammuto, R.F. and O’Connor, E.J. (1992) Gaining advanced manufacturing technology’s benefits: The roles of organization design and culture. Acad. Management Rev. 17(4), 701–728. Zhong, J. and Majchrzak, A. (2004) An exploration of impact of cognitive elaboration on learning in ISD Projects. Information and Technology Management 5(1/ 2), 143–160.

Further reading Goodman, E.A., Zammuto, R.F. and Gifford, B. (2001) Utilizing the competing values framework to understand the impact of organizational culture on the quality of work life. Org. Devel. J. 19(3), 58–68.

ORNIT RAZ, MIT Sloan School of Management, Massachusetts Institute of Technology E40-339, 77 Mass. Avenue, Cambridge, MA 02139-4307, USA. E-mail: [email protected] Ornit Raz is Postdoctoral Research Associate at Massachusetts Institute of Technology, Sloan School of Management and Post Doctoral Fellow at The Samuel Neaman Institute for Advanced studies in Science and Technology, Technion, Israel. Her research interests include theoretical and practical issues regarding industrial clusters, communication networks and the relationship between knowledge management and information technologies. Her current study deals with communication networks established among bio-technology companies in the Boston region.

ALBERT I. GOLDBERG, Technion – Israel Institute of Technology, Faculty of Industrial Engineering and Management, Haifa 32000, Israel. E-mail: [email protected] Albert I. Goldberg is Associate Professor of Industrial Sociology at the Technion – Israel Institute of Technology in Haifa, Israel. His research focus is on knowledge management, entrepreneurship, and industrial clusters.

European Management Journal Vol. 24, Nos. 2–3, pp. 199–205, April–June 2006