exploring the critical success factors for developing and implementing a predictive capability in...

■ Research Article

Exploring the Critical Success Factors forDeveloping and Implementing APredictive Capability in Business

Harri Jalonen1* and Antti Lönnqvist2

1Business Information Management, Turku University of Applied Sciences, LOIMAA, Finland2Business Information Management and Logistics, Tampere University of Technology, Tampere, Finland

Predictive capability (when implemented in a business context) refers to the early recognition of businessopportunities and threats, improved customer intimacy, and agile reaction to changes in the business environ-ment. In this article, we propose that developing and implementing a predictive capability depends on a changeprocess that is only implementable under certain conditions. Based on a review of the research literature andinterviews, three critical success factors relating to the successful implementation of the relevant change processwere identified. They are (1) identifying the change drivers; (2) organizing vision for the change; and (3) thecapacity to execute change. These three factors were further divided into 11 dimensions, which are as follows:(1a) increasing networking; (1b) changing customer needs; (1c) increasing elements of the knowledge economy;(1d) development of the information technology; (2a) strategic benefits; (2b) transactional benefits; (2c) informa-tional benefits; (3a) strategic capabilities; (3b) management capabilities; (3c) knowledge capabilities; and (3d)operational capabilities. The findings suggest that Finnish manufacturing companies are willing to start thechange toward developing and implementing a predictive capability. However, they are currently only at thestage of orienting toward this new managerial paradigm. Copyright © 2011 John Wiley & Sons, Ltd.

INTRODUCTION

Planning for the future has a long history. From theearly work of WalterQ1 Shewhart (1920s and 1930s),management researchers and practitioners havestressed the importance of making attempts topredict the future state-of-affairs. Over the decades, awide array of methods for measuring, analyzing,interpreting, and presenting data in a way thatenables managers to predict the development of keyaspects of their business operations have been intro-duced. Nowadays, we have come to a time wherethe field is teeming with varied terminology, such asperformance measurement (Soosay and Chapman, 2006),process performance measurement (Nenadál, 2008),business activity monitoring (Peipert, 2005), businessevent processing (Zeng et al., 2007), business processintelligence (Grigori et al., 2003), complex event processing

(Luckham, 2007), predictive analytics (Hair, 2007),competing on analytics (Davenport and Harris, 2007),real-time business intelligence (Watson et al., 2006; Sahayand Ranjan, 2007 Q2), real-time knowledge management (ElSawy and Majchrzak, 2004), and predictive businessoperations management (Castellanos et al., 2006).The promises of prediction are obvious. On the

one hand, predicting means that managers canalter the likelihood that negative outcomes willoccur by intervening prior to their occurrence.Specifically, being able to develop accurate predic-tions may mean, among other things, the abilityto prevent the loss of a customer to a competitoror to initiate proactive measures to mitigate costsof possible interruptions in the company’s oper-ational processes. On the other hand, predictionmay also lead to the early recognition of newopportunities. It means, for example, the ability toreact agilely to changes in demand for a particularproduct or respond to an emerging customerneeds. These examples illustrate the fact thatpredictive capability does not lead to results with-out the ability to act based on the “outputs” ofthe predictive capability.

*Correspondence to: Harri Jalonen, Business InformationManagement, Turku University of Applied Sciences, LOIMAA,Finland.E-mail: [email protected]

Knowledge and Process Management (2011)Published online in Wiley Online Library(www.wileyonlinelibrary.com) DOI: 10.1002/kpm.386

Copyright © 2011 John Wiley & Sons, Ltd.

Journal Code Article ID Dispatch: 03.11.11 CE: Rechelle G. RazonK P M 3 8 6 No. of Pages: 13 ME:

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In a more detailed manner, predictive capabilitycan be defined as specific mode of operation andits supporting knowledge system, used to rapidlyproduce analytical information based on eventdata from business processes mainly to supportoperational decision making (Jalonen and Lönnqvist,2009). Thus, business events are seen as the rawmaterial for a specific chain of acts that constitute anemergent whole. An emergent whole means thehigher-level behavior generated by the combinedeffect of the events. Because of the emergence, evenapparently quite unrelated events, small in them-selves, may, in the right conditions, combine to forma chain of events with sometimes quite unexpectedeffects. Think, for example, that in a company produ-cing dairy products, the lift of a fully automatedlogistics center stops for hours. For products requiringcold storage, the company has no choice but toforward the products to a waste disposal site fordestruction. The direct costs of the operation reachtens of thousands of euros. In addition to the spoileddairy products, the company incurs costs from abreach of the service level agreement. The company’sreputation as a reliable supplier also suffers a blow,and its restoration generates costs with long-termeffects. Events, such as the one described earlier,acquire real business significance only in combinationwith other events. It is this emergent whole that repre-sents either a threat or an opportunity for the company’sbusiness. Therefore, it is arguable that predictivecapabilities not only lead to operational efficiencybut also are critical to a company’s competitiveadvantage. In other words, a company’s predictivecapability and its resulting ability to adapt quicklyto changing events is a key factor enabling firmsto both achieve and also maintain competitiveadvantage (Castellanos et al., 2006).

The development and implementation of apredictive capability can be seen as an attractiveoption for all kind of business. However, needlessto say, the attractiveness is not the same thing asthe company’s ability to change its business modes.According to change management literature, a hugemajority of change initiatives fail (Higgs andRowland, 2005). There is a clear tendency for peopleand organizations to resist change. Reasons behindresistance to change are various. Kotter andSchlesinger (1979), for example, summarize four pre-vailing reasons for resistance to change: self-intereststhat affect decisionsmore than organizational interests,misunderstandings because of communication pro-blems, low tolerance to change, and disagreementabout the reasons for change. In addition, resistancecan occur in myriad forms. A resistance for changecan be conscious or unconscious and vary fromorganized political activity, at the micro or macrolevels, to a simplewithdrawal of commitment (French,2001).

Bringing together the promises of seeing inadvance and the organizational tendency to resist

change, we pose the following research question:what are the factors currently identified as important totransforming manufacturing organizations towardpredictive capability? We use the concept “predictivecapability” to refer to a comprehensive managementapproach—a way of thinking and acting. It is a ques-tion of a deliberate change to organization’s technicaland organizational subsystems. Adapting Leonardiand Barney (2008), we argue that predictive capa-bility arises “at the intersection of social andmaterial phenomena.” Therefore, a relevant researchquestion should deal with factors that are at stake ina change process toward a predictive mode ofoperations.The extant literature on predictive capabilities

(sometimes also referred to as predictive business,business event processing, real-time businessintelligence, real-time knowledge management,etc.) is fairly descriptive and, in some instances,commercially motivated. There is a lack of researchregarding the theme in general. Especially empiricalresearch is non-existent, perhaps because of thenewness of the issue and, thus, the lack of compan-ies to be studied. Thus, there is a lack of knowledgeon why companies do or do not choose to developpredictive capabilities. Our paper makes a contri-bution to this stream of research by identifying—based on an interview study—the issues affectingthe organizational change process that results inthe development and implementation of a predictivecapability. In this paper, the term predictive cap-ability refers to a type of operational decisionmaking and the development of business pro-cesses based on business event analysis. Theprediction of product demand and market shares,the evaluation of macro-economic development,scenario work to probe the changes in the com-pany’s business environment, and other similaractivities aiming to analyze the future are leftoutside the scope of the paper.This paper is structured as follows. In the second

section, the relevance of the concept of predictivecapability is presented and the concept is discussedand analyzed. The third section first introduces theresearch design and then explains the analysisframework. The fourth section discusses the criticalsuccess factors for the change that results in thedevelopment and implementation of a predictivecapability in an organization. This section isconstructed based on prior findings from the litera-ture as well as on the interviews carried out. Finally,in the fifth section, conclusions are drawn.

THE DEFINITION AND RELEVANCE OFPREDICTIVE CAPABILITY

The possession of a predictive capability refers tothe existence of an activity within an organizationthat aims to eliminate guesswork and surprises

2 H. Jalonen and A. Lönnqvist

Copyright © 2011 John Wiley & Sons, Ltd. Know. Process Mgmt. (2011)DOI: 10.1002/kpm

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from business processes, to identify opportunitiesand threats at an early stage, and to respond quicklyto changes in such factors as demand and offer orchanges in the financial and commodities markets(DeLone and McLean, 2003; Davenport and Harris,2007; Jalonen and Lönnqvist, 2009). The ultimategoal of predictive capability is considered to be theattainment of competitive advantage throughincreasing operational agility by applying better in-formation and knowledge management methodsand practices. Information refers herein to data thathas a form and a context of use, which carriessome kind of meaningful content, and is intendedto be communicated (Grover and Davenport,2001). Information becomes knowledge when thereceiver interprets it in a specific context (Grover andDavenport, 2001)Q3 . Following the arguments by ElSawy andMajchrzak (2004), however, we suggest thattraditional methods for knowledge managementrequire rethinking. When information provision, cycletimes, and action become faster to the point of beingnear-real-time, it is important that knowledgemanagement methods support rapid identification,interpretation, articulation, and resolution of issuesarising from various internal and external sources.Therefore, from the viewpoint of information andknowledge management, we argue that predictivecapability can be divided into three phases thatmay be described as observation, analysis, andoptimization (Jalonen and Lönnqvist, 2009).

Observation refers to active and systematic informa-tion collection activity making use of the company’sinternal (e.g., ERPQ4 systems) and external (e.g., newsservices) sources. By combining the informationcollected from different sources, managers can obtainan improved view of the status of the company’soperative processes. In practice, this could mean thata process owner is provided with a real-time visualdisplay over the business process. This could be used,for example, for quickly observing the bottlenecks ofthe process. If, for example, the realized throughputtime of a delivery process differs from preset limits,the process owner receives an automated alarmmessage, which makes it possible to react quicklyand make corrective actions to prevent problems forthe customer. This helps the process owner to obtainan understanding on the chain of events that hasbegun or is about to begin, what kind of informationis needed to solve the problem, and where can thisinformation be located (Armenakis and Bedeian, 1999).

Observation creates the basis for the analysis ofevents. The goal of the analysis is to understandthe relationships between events. In the analysisphase, information generated from the chain ofevents is combined with knowledge, which hasbeen created during prior chains of events andrecorded in different information systems (IS). It isassumed that there are always some similaritiesbetween different events, and thus, the informationabout a certain event can be used in analyzing

another one. In practice, this refers to patterns andrules that can be utilized to identify manageriallyrelevant events from the flow of events (Luckham,2007). The analysis of events aims to clarify thereasons leading to higher level behavior. Thus, whatis essential is the ability to examine, at many levels,the information and knowledge created in theprocesses. Luckham (2007) writes about recognizingvertical causality between business events in differ-ent levels. It means, for example, the underlyingcause for the deviation in the throughput timeof the delivery process can be discovered byexamining the process at different stages (e.g., order-to-production, production-to-shipping) separately.The analysis of events creates a foundation for

optimizing activities. Optimization refers to pursu-ing the best possible outcome under the givenconstraints. Because optimization is a future-orientedactivity, it can be assumed that a versatile knowledgebase is required to carry it out. In addition to informa-tion and knowledge, some intuition is also needed.Intuition does not refer to the opposite of theanalytical process but more to a supplementary formof knowing. Intuition can be seen as a kind of a leap,in which a decision maker takes some distance to theproblem at hand and solves it in a novel way. In thecontext of predictive capability, analytical “insight”for the future can be supported by offering thedecision maker tools for modeling, simulation, andmaking what-if analyses (Lundberg, 2006).However, it is unclear to what extent the different

approaches—especially the terms mentioned in theintroduction, that is, performance measurement,process performance measurement, business activ-ity monitoring, business event processing, businessprocess intelligence, complex event processing,predictive analytics, competing on analytics, real-time business intelligence, real-time knowledgemanagement, and predictive business operationsmanagement—are interrelated and what theirspecific added value is when compared with moretraditional approaches. To have a more precise ideaof the meaning of a predictive capability, it is usefulto compare its characteristics with more traditionalapproaches to business management. The compari-son has its problems, though, because traditionalapproaches too are ambiguous. Business intelligence,for instance, may be understood in several differentways (Pirttimäki, 2007). It is sometimes seen as amethod of analyzing the business environment (e.g.,competitor analyses, market studies), which mainlyproduces qualitative information. At other times, itis taken to mean an IT application with which theinformation contained in the company’s knowledgesystems is refined into visual reports for the manage-ment. In spite of the related challenges, a comparisonbetween the typical characteristics of differentmethodsmakes it possible to demonstrate differencesbetween approaches and thus also to discuss whetherthere is a need for a new type of predictive capability. In

Predictive Business 3


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the following, the characteristics of business intelligenceand performance management are described andthen compared with predictive capability.

Business intelligence generates analyses andreports on trends in the business environment andon internal organizational matters. Analyses maybe produced systematically and regularly or theymay be ad hoc ones, related to a specific decision-making context. This knowledge is utilized not onlyby decision makers at different organizational levelsbut also by experts (e.g., exploitation of a newsservice). The process results in the generation ofboth numerical and textual information.

Performance management is based on thecontinuous measurement of objective factors identi-fied as being important (see, e.g., Lönnqvist, 2004).This is carried out at both operational and strategiclevels, which means that the objects of measurementvary as well. As a rule, measurement results arenumerical, although qualitative information is alsoused, for example, in customer satisfaction measure-ments. The results of the indicators are oftenreported in some sort of dashboard, sometimesincluding a possibility for the in-depth examinationof more detailed data. Compared with businessintelligence, the main difference lies in the fact thatperformance management is limited to a selected setof measurable items, whereas business intelligencefathoms a larger field through variables that havenot been delimited beforehand. On the contrary, itsometimes seems that in practice, businessintelligence products are really just sophisticatedindicator reporting systems, and there are thus manysimilarities as well.

When the approaches described earlier are comparedwith the concept of predictive capability, many similar-ities are found: in fact, all of the approaches aim toproduce information to support decisionmaking.How-ever, the purpose of the information produced bypredictive capability is to serve operational-levelpersonnel rather than management. It emphasizesthe speed of analysis—the striving for real-time moni-toring and decision making. In addition, the object ofmonitoring is a large number of process-level events.The number of monitored factors is larger than inperformance measurement, and this also helps topoint out unexpected events. In the analysis of predict-ive capability, the ITsolution has a central role, whereasmeasurements and business intelligence can begathered even with simple technical tools, in principle.TableT1 1 describes some of the essential characteristics inthe different approaches.

The novelty value of predictive capability wouldseem to lie in the systematic examination of busi-ness process data on a more detailed level than hasbeen implemented before and also with analysesthat support quick decision making and develop-ment of operational activities. Even though thispaper does not evaluate the functionality or useful-ness of the applications on the market, it should be

noted that different solutions are already available,for example, complex event processing tools andtechniques for analyzing and controlling interre-lated events (Luckham, 2007).Furthermore, the development and implementa-

tion of a predictive capability can be approachedsimultaneously as a practical and an epistemicchallenge (Jalonen and Lönnqvist, 2009). It is a prac-tical challenge because developing and implement-ing a predictive capability presupposes a change inthe company’s modes of operation. In particular, itconcerns the company’s ability to shift fromplanning-oriented operation (plan—execute) toresponse-oriented operation (sense—respond) (Welkeet al., 2007). Although the planning-oriented modeof operation relies on the operators’ capacity toremove uncertainty related to the future and the busi-ness environment through rational planning, theresponse-oriented approach emphasizes the com-pany’s sensitivity to internal and external events. Thissensitivity may appear as quickened action-learningloop (cf. El Sawy andMajchrzak, 2004) throughwhichthe company adapts its operations to suit internal andexternal events.Predictive capability also entails an epistemic

challenge because it concerns the company’s abilityto exploit information and knowledge in differentproblem-solving and decision-making situations.This epistemic challenge may be analyzed by divid-ing knowledge management into knowledge exploit-ation and knowledge exploration. Knowledgeexploitation is based on the knowledge and know-how incorporated in the established routines andmodes of operation, whereas knowledge explor-ation emphasizes the identification of newopportunities and alternatives (March, 1991). Sym-bolically, exploration may be seen as a sort of“expedition” that not only visits new places andcreates new knowledge but also potentially con-structs a basis for reforming modes of thinkingand operation (Holmqvist, 2004). Problems, how-ever, arise from the fact that in practice, there isalways a certain amount of tension between effi-ciency and creativity. This is because of factors suchas the different “yield expectations” betweenroutines and expeditions. Whereas routines createa feeling of security and continuity, expeditionshave an uncertain basis and involve various risks.This despite in the long-term, failure to explorenew ideas and alternative modes of operationmay paradoxically result in the iteration of risksand give rise to a development difficult to handle.(See more on organizational paradoxes, e.g., Blood-good and Bongsug, 2010.)

THE RESEARCH DESIGN

The methodology of this exploratory study can becharacterized as qualitative in nature, an approach



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that is common in studies whose purpose is to gainunderstanding of how practitioners “define the situ-ation” (Marshall and Rossman, 2006). Qualitativemethods were chosen because they are flexible andenable in-depth exploration of perceptions from alimited number of enlightened individuals.

A logical way to study the change involved indeveloping and implementing a predictive capabilitywould have been to study companies that arecurrently implementing or have developed andimplemented predictive capabilities. However, pre-dictive capability is such a new managerial approachthat the authors were unable to locate and gain accessto such companies in Finland. Thus, instead, a groupof potential future applicant companies were selectedto be interviewed. The selection of companies wassupported by an IT service company that hadidentified the selected companies as potential can-didates for initiating a change process involvingthe development and implementation of predictivecapabilities.

To explore informants’ perceptions on the factorscontributing to the change process, we applied asemi-structured group interview technique. As thestudy is exploratory in nature, the research waslimited to a fairly small number of interviewees.The empirical data were collected by conductingfive semi-structured group interviews with industryexperts in five Finnish manufacturing firms. Theinterviews were conducted in January–March 2009.A total of 12 industry experts were interviewed.This may result from the careful recruiting processof the interviewees. The interviewees were selectedon the basis of their positions and their experience.Their professional titles include the following: VicePresident, Head of Unit, IS/IT Manager, Develop-ment Manager, Production Director, and MarketingDirector. Based on the experience of the intervie-wees, it is reasonable to expect that they have “some-thing to say on the topic” (Rabiee, 2004). Eachinterview lasted approximately 60–120minutes andwas recorded on an audio tape.

The topics of the interviews were determined bythe findings of an earlier conceptual study by theauthors and by issues identified in the organizationalchange literature. As a concept, “organizationalchange” can be understood as an outcome or as aprocess. Organizational change means difference inone or more sub-systems, such as structures,processes, IS, and culture (Hildén, 2004; Markova,2006). In this paper, we use organizational changereferring to a process where a company changes froma current state to a wanted future state to increase, forexample, its competitiveness or flexibility (Markova,2006; Arita et al., 2007).Conceptualizing the creation and implementation

of predictive capabilities as a change process leadsto the research question regarding the factorsrelated to the success of this type of organizationalchange. However, given the ambiguity of the notionof the organizational change (e.g., Weick and Quinn,1999), it is clear that the evaluation of the success ofthe organizational change process is a difficult task.Given that predictive capabilities are more or lesslinked to IS, it is reasonable to lean on existingresearch of IS-related change. A brief analysis ofthe literature of the IS-related change processesshows that factors leading to success (or failure)are complex and do not occur alone. IS-relatedchange is always uncertain (because of complexinteractions between the elements in the designand change context), ambiguous (the nature andmeans of IS change vary over time), and hard(because of the scale and scope of the effort neces-sary to mount the change) (Lyytinen and Newman,2008). Thus, any list of the factors of the successfulchange is more or less context-dependent social con-structs and consequently subjective (e.g., Williamsand Ramaprasad, 1996; Wilson and Howcroft,2002). Despite the difficulties, there are fewmethodsthat can be utilized to evaluate the success of theIS-related change. We adopted the critical successfactors (CSFs) method. CSFs refer to “those things thatmust go well to ensure success for an organization”

Table 1 Characteristics of different approaches to management

Approach Focus on attention Aim of activityExploiting

organizational levelTime span ofexamination

Businessintelligence

Phenomena related to thedecision-making situation

To identify trend in thebusiness environment and toproduce analyzed informationfor the decision maker

Management andexperts

Monthly to yearly

Performancemeasurement

Factors identified asimportant (performanceobjectives), for which anindicator has been designed

ERP Q5, monitoring objectiveattainment

Management andsuperiors at differentorganizational levels

At process level:daily to monthly; atcorporate level:monthly to yearly

Predictivecapability

Predictable and unexpectedevents in business processes

To quickly gain knowledge onsignificant events with theobjective of fixing the problemor exploiting the opportunity

Operative personnel Continuousmonitoring



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(Boynton and Zmud, 1984). Nevertheless, we do notargue that CFFs method is (not) context dependentor objective. Instead, we think that the value of theCSFs method is that it is familiar to the senior man-agement.Q6 Rockart (1979) and Boynton and Zmud(1984), among others, have noted that the CSFapproach represents an acceptedmethodology for cor-porate strategic planning. Given that the “predictivecapability” refers to a comprehensive and strategicmanagement approach, we consider reasonable touse a methodology that fits into the context of thestudy. We found that interviewed managers andexperts were well used to assess “pros and cons”(possible success and failure factors) of the plannedand/or ongoing change processes from the perspec-tive of the CSFs.

The CSF method has been used frequently in thefield of the IS research. Bailey and Pearson (1983),for example, have found seven factors that impactthe success of the information system. The list givenby Bailey and Pearson includes factors such assystem quality, information quality, information use,user satisfaction, individual impact, service quality,and conflict resolution. DeLone and McLean (1992)have the same kind of categorization. Themodel givenby DeLone andMcLean consists of six factors that canbe used to measure the success of information system.They are system quality, information quality, use, usersatisfaction, individual impact, and organizationalimpact. However, from the point of view of the currentstudy, both of the aforementioned approaches arelimited because they focus mostly on the technicaldimensions of the IS or on users’ perceptions of theusefulness of the information system. The weaknessof many CSF techniques used in the field of the IS isthat they do not consider (or consider poorly) the rai-son d’être of the IS-related change process. This is abit peculiar because, in the mainstream of the changemanagement literature, the legitimacy of the under-lying reason for the change is seen as a crucial elementof the successful change (e.g., Schein, 1987). Further-more, there are many CSF techniques that do not takeinto consideration the overall contribution of IS to theorganizational goals or the effectiveness of the systems(Li, 1997). Hence, a broader approach is needed toassess success factors for the development andimplementation of a predictive capability, which wethink manifests itself as the realized difference inorganizational structures, processes, IS, and culture. Inother words, predictive capability is an organizationalchange endeavor that has impacts and is dependenton organization’s technical and organizational subsys-tems, and its surrounding environment.

Having conducted a review of the change man-agement literature, it became evident that successfulorganizational change incorporates and managestechnical, organizational, and environmentalperspectives concurrently (e.g., Kanter et al., 1992;Champy, 1995; Siegal et al., 1996). Furthermore, theliterature presents a wide range of models for

managing successfully organizational change pro-cesses (see, e.g., Siegal et al., 1996). On the basis ofour literature review, we propose that successfulchange requires the realization of three factors. First,there must be motivation for the change. Schein(1987) speaks about disconfirmation, by which hemeans that members of organization experience aneed for change that, in turn, motivates them tostrive for change. In other words, people perceivea gap between a current and a desired future state.We call this success factor the driver for change. Sec-ond, the desired future must be articulated (Beck-hard and Harris, 1987). Typically, this will be inthe form of a vision statement. Adapting the ideapresented by Swanson and Ramiller (1997), we usethe notion of organizing vision, which refers to afocal community idea for the application of thenew mode of operation. Organizing vision for changecreates purposefulness to the process and is henceconsidered as the second success factor for thechange. Third, managing capabilities must be present.These capabilities refer to both the psychologicalissues related to change and the design and structuralissues of change efforts (Burke, 1994). We refer to thethird success factor the capacity to execute change. Weargue that if any of these three factors aremissing thenthe basis for successful change is absent and, as a re-sult, the change process fails (see Table T22).The framework depicted in the Table 2 guided the

literature review, the preparation and execution ofthe interviews, and the data analysis. The empiricaldata collected in these interviews were analyzed byusing the pattern matching logic presented by Yin(2003). In this study, pattern matching refers to amethod where interview material was interpretedby the framework depicted in the Table 2.

RESULTS: THE CRITICAL SUCCESSFACTORS FOR THE DEVELOPMENT ANDIMPLEMENTATION OF A PREDICTIVECAPABILITY

During the analysis stage, each of the three successfactorswas decomposed into three to four dimensions.In total 11 dimensions were found. The links betweenthe 3 success factors and their 11 dimensions to thepredictive capability are presented and discussed insections 4.1–4.3.

Table 2 The framework for successful change process

Changedriver

Vision forchange

Changecapacity Outcome

OK OK OK A solid basis forsuccessful change

Missing OK OK Needless changeOK Missing OK Purposeless changeOK OK Missing Frustration



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Identifying the change drivers

The term change driver is used here to describethose factors that influence positively on the devel-opment and implementation of a predictive capabil-ity. The identification of change drivers is essentiallysubjective reflecting a company’s understanding ofthe problems and the possibilities it may encounterin the near future. Change drivers provide boththe impulse and motivation for the change. There-fore, a change without clear drivers can be viewedas an unnecessary endeavor. Based on our literaturereview and interviews, four change drivers forpredictive capability were identified. They areincreasing networking, changing customer needs, risingof the knowledge economy, and development of informa-tion technology.

Increasing networkingThe interviews confirmed that focus of the businessis not an individual company but the value-creatingnetwork, within which different economic actors—suppliers, partners, customers—work together toco-produce value. Whether these relationships arebased on co-operation or on competition (Lechnerand Dowling, 2003), the outcome is complexnetworks with a great number of interdependencies.Networks define the infrastructures within whichthe business, its suppliers, and its customers inter-act. Typically, networks are built because two ormore companies hold complementary competenciesthat can be used to widen the offerings to the custo-mers. However, networks are also significantchallenges for the development and implementationof a predictive capability. This is because network im-plies boundaries that are always potential hurdles forthe speed of information exchange. Therefore, it isnot a surprise that the interviewees identified informa-tion delays caused by organizational boundaries suchas business units and functions. The informationdelays were reported to be the one of the main causeof the network management problems. Several inter-viewees recognized considerable difficulties that arisewith material flowmanagement because of the lack ofreal-time information. From the predictive capabilitypoint of view, business operations should be deliveredwith minimum time latency because the value ofinformation typically decreases as time passes(cf. Hackathorn, 2004). The more the network hasboundaries, themore effort should be put onminimiz-ing their effects on observation and analysis ability. Itcan be argued that the change to predictive operationmode remains elusive goal without deep understand-ing of how boundaries affect network’s ability to ob-serve and analyze information in function of time.

Changing customer needsAll the interviewees pointed out that the customersare the main focus of successful business. They char-acterized the business environment as dynamic in

nature, by which they referred to unexpectedchanges in customer needs and preferences. Manycustomers were reported to prefer more tailoredproducts and services, and at the same time, theirservice response-time expectations have shorteneddramatically. According to interviewees, customerloyalty is based more and more on company’sability to manage and mitigate the effects of nega-tive service delivery. Extremely important is tohandle quickly and effectively customer complaints(cf. Ashley and Varki, 2007). However, tacklingaforementioned issues is not a simple task. In thelight of predictive capability, two requirements areidentified. On the one hand, companies should under-stand the need for analysis of customer behavior to find alink between causes and events. This improvescompanies’ possibilities to develop new customerup-sell and cross-sell and prevent recurringproblems (Lundberg, 2006). On the other hand,companies should understand the limits of analyzing thepast. To be proactive requires real-time process andperformance analytics and alarming. By real-time ana-lytics, it is possible, for example, to proactively informthe customer of the delay and thereby strengthen thecustomer relationship (Lundberg, 2006). Understand-ing the role of the information from the past andpresent and their relation to the future course of eventwere seen as a key determinant for successful changetoward predictive capability.

Rising of the knowledge economyThe knowledge economy implies that knowledgehas become the core resource, capability, and assetfor many companies. Knowledge-based view ofthe firm, for example, stresses that knowledge isthe most important source of competitive advantage(Grant, 1996). This is also a reality for the companiesinterviewed. The value of knowledge is that it pro-vides for individuals and companies an ability toperform specific task more efficiently. Knowledgesaves time, efforts, and money. However, theknowledge economy has paradoxical effects oninformation behavior. The main paradox is “a surfeitof information and a paucity of useful information”(Edmunds and Morris, 2000). On the one hand, theinterviewees reported a problem of “informationoverload,” which is the result of the individual’s andorganization’s limited ability to assimilate the greatamount and variety of information effectively (Epplerand Mengis, 2004). In the period of increased richnessand reach of information, the risk is that companiesfind themselves “bombarded with information—toomuch, too fast, too late” (cf. Edmunds and Morris,2000). On the other hand, several interviewees judgedthat their companies may suffer “knowledge is power”syndrome. This syndrome manifests itself as a compe-tition between individuals or business units (Hardagonand Sutton, 2000). Typically, the result is functionalsilos and knowledge hoarding, which causes alsoknowledge initiatives to fail (Orlikowski, 1993).



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Furthermore, it is also important to remember thatinformation seeking is always a costly effort. The op-timal amount of information is reached when furtherefforts are not worth their cost (Birchler and Bütler,2007). To avoid information overload and to preventknowledge hoarding, companies need to adopt pro-cedures that target to reduce the time and cost spenton finding and sharing valid information.

Development of the information technologyInformation overload is an increasing problem thatcauses various consequences. According to theinterviewees, these consequences vary from indi-vidually suffered infoglut to company-level uncer-tainty concerning information reliability. Althoughinformation overload causes problems, there is alsoa technology developing that allows mitigating thedetrimental effects of increasing information. Fromthe predictive capability view, the most promisingtechnology is push information technology thatexploits intelligent agents. The value of this technol-ogy is that intelligent agents can scan various infor-mation sources and automatically route importantinformation to users (Edmunds and Morris, 2000).Instead of just searching information, intelligentagents aggregate and analyze information basedon users’ requirements.

Intelligent information technology can be definedas the technology that meets the requirements of anobserve-orient-decide-act loop (El Sawy andPavlou, 2008). Observing support means that IT inte-grates information from various sources, captureskey performance indicators, and provides visibilityinto business processes. Orienting support meansthat IT displays information through graphicaldashboards, provides drill-down functionality, andgenerates various alerts. Deciding support is basedon IT’s ability to carry out what-if analytics and stat-istical analysis. Acting support refers to IT-basedcommunication (before decisions) and outcomestracking (after decisions). Several intervieweespointed out the importance of the real-timevisualization of business processes. They agreed,for example, that the real-time graphics demonstrat-ing the state of the order-to-delivery processfunctions can reveal patterns that would otherwiseremain undetected (cf. Tyman and Huang, 2003).Visualization techniques were estimated to havepotential to separate irrelevant information fromthe relevant ones (Chung et al., 2005). However,although many interviewees were quite optimisticabout the technology-based process visualization,it is important to remember that intelligent anduser-friendly information technology should beseen as a necessary—not a sufficient—change driverfor successful change toward predictive capability.The exploitation of the potential of informationtechnology is constrained by various social andcultural factors (Markus, 1983). In short, the moreopen the information exchange is, the more the

possibilities open up for information technologyusage and impact.

Organizing vision for change

Achieving organizational change depends on thecreation of a strong vision for the future (Barrett,1994) Q7. Desired future state can be expressed inwords that describe the benefits that the companycan gain when the vision is achieved. Together withthe change drivers, vision creates purposefulness tothe change process. Benefit is a significant improve-ment in company’s performance. In this research,three predictive capability-related benefits wereidentified. They are strategic, transactional, and infor-mational benefits.

Strategic benefitsOn the basis of interviews, three strategic benefitswere identified. They are solid decision-making cul-ture, better customer relations, and enhancement ininnovation and learning capability. Predictive busi-ness practices improve the decision-making culturein two ways. On the one hand, the accuracy ofprogrammed and routine decisions (cf. Daft, 1994)is improved because of applying rigorous businessrules and patterns (Luckham, 2007). Decisions aremade quickly and timely by removing unnecessarymanual interface points of decision cycles (Indart,2005). On the other hand, predictive business prac-tices also address the non-programmed and uniquedecisions (cf. Daft, 1994). For example, a fulfillmentof the requirements of observing, orienting, decid-ing, and acting (El Sawy and Pavlou, 2008) increaseboth the company’s ability to choose betweenvarious alternatives and to take organized actions(cf. Brunsson, 1985). Many interviewees agreed thatgreater visibility into the company’s processescan lay a good basis for coordination among cross-functional, which, at its best, would lead to a singleversion of “the truth” (cf. Frolick and Ariyachandra,2006). Predictive capability leads also to benefits withrespect to customer relations. It helps companies toestablish, sustain, and retain customer relationships(Radhakrishnan et al., 2008) Q8. Based on real-timeprocess monitoring, predictive capability increasesthe company’s overall ability to react responsivelyto customer needs. The enhanced level of customerservice implies, for example, avoidance or reductionof the impact of negative events, for example, failingSLAs Q9and delays in shipments. Moreover, actingproactively before problems arise reduces customercomplaints, improves satisfaction levels, and lowersthe risk of customer defection (Lundberg, 2006).Predictive capability has positive influence onorganizational learning and innovation capability aswell. Re-thinking operational problems and findingpotential solutions in terms of business events,supported by proper information technology,



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provide the basis for an innovative organizationalculture. The innovative organizational cultureenables people to think “outside-of-the-box” andto get new perspectives on old problems. As aresult, innovative and learning-oriented companiesforesee environmental andmarket changes andmakequick adjustments when needed (Radhakrishnanet al., 2008).

Transactional benefitsThe interviewees recognized three various transac-tional benefits that are cost efficiency, effectivesupply chain relations, and overall process transpar-ency within the supply chain. Cost efficiency is basedon high-quality and real-time information aboutinternal and external operations that enable toautomate processes and tasks, to reduce administra-tive costs, to lower inventory levels, and to improveresource allocation (Mirani and Lederer, 1998).Effective supply chain relations are founded on increas-ing the quality, amount, and timeliness of informationtransmitted across company boundaries (Clemonsand Row, 1993). The right-time information reduces,for example, procurement and coordination costswith suppliers (Elbashir et al., 2008). At its best, thecoordination leads to the overall process transparencywithin the supply chain. All of the intervieweesagreed that the process transparency in whole valuechain is a desirable goal. The main benefit of theprocess transparency is eliminating the whip effects,that is, surprising fluctuations, which are mostlyresults of wrong demand prognoses and informationasymmetries (Arndt, 2005).

Informational benefitsAccording to interviews, predictive capability offersinformational benefits that allow companies to dealwith the challenges of the knowledge economy.Adapting the idea presented by Mirani and Lederer(1998), informational benefits of the predictivecapability can be classified as information access,information quality, and information flexibility.Information access enables decision makers to obtaindifferent internal and external information. Informa-tion quality implies that decision-making informationis useful, accurate, and reliable. Information flexibilityrefers to decision makers’ ability to manipulate thecontent and format of retrieved information. Withintelligence IT tools, such as intelligent agents,dashboards, drill-downs, and OLAPQ10 views, decisionmakers have fast and easy information access, betterinformation quality, and more flexibility to processretrieved information. In other words, the companyhas introduced an organized and systemic processto acquire, analyze, and disseminate informationfrom internal and external information sourcessignificant for operative decision making (Lundberg,2006). This also narrows the gap between the infor-mation available and the information required foran optimal decision (Galbraith, 1973) and helps

to find an appropriate balance between the informa-tion-seeking costs and the information gains (Birchlerand Bütler, 2007).

The capacity to execute change

Identifying change drivers and establishing thevision lay down a good basis for the change process.However, what is also needed is a change capacityto transfer these drivers and visions into action.The change capacity is defined as consisting ofvarious intangible capabilities that contribute tothe execution of the change. Based on the literatureand interviews, the change capacity to predictivebusiness consists of strategic, management, knowledge,and operational capabilities.

Strategic capabilitiesThe interviews confirmed the well-known truth thatan organizational change is a risky and difficultendeavor. Thus, it is not surprising that in reality,many change processes fail. One main reason forfailure is the way how managers view organiza-tions, their members, and the function of technologywithin them (Bostrom and Heinen, 1977). It seemsthat failing organizations suffer the lack of strategicview. There is large amount of research that supportsthe argument that company’s success in changeprocess depends highly on its strategic capability(e.g., Doz and Kosonen, 2008). Strategic capabilitiesrefer to the set of capacities, resources, and skillsthat create a long-term competitive advantage forthe company. The interviewees stressed thatespecially in the rapidly changing environment,the change will not succeed without strategiccapability, which means, in practice, that companieshave ability to identify, evaluate, and rank predict-ive business opportunities based on their long-termbusiness impact and short-term execution risk,including also organizational readiness and techno-logical maturity (cf. Huang and Hu, 2007; Teece Q11

et al., 1997). Strategic capability provides ability to linkthe rationale of the change to company’s everydaybusiness.

Management capabilitiesAll other things being equal, people will resistchange (Markus, 1983). It is quite obvious that thechange to predictive business does not happen inisolation either. It is a change process that needs tobe managed properly. Based on the literature andthe interviews, management capabilities can bedivided into two main types: management commit-ment and strong leadership. Managementcommitment refers to change process design andredesign (Henderson and Venkatraman, 1993), changemanagement program with clear scope (time, cost,people, quality) for the change and maintaining thescope (Vidyaranya and Gargeya, 2005), and adequate



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financial and other resources for change activities(DeLone andMcLean, 2003). The roles of strong leader-ship are to provide a sense of purpose and create anengaging environment for the change (Snowden andBoone, 2007), to empower people to win obstacles(Kotter, 1996), and to help to manage expectationsand avoid power conflicts (Markus, 1983). Severalinterviewees pointed out that the goal of managementand leadership efforts is to remove political, econom-ical, mental, and organizational barriers to proactivemindset and practices. Together, managementcommitment and strong leadership create a windowof opportunity for the change toward predictivecapability.

Knowledge capabilitiesAs stated earlier, the importance of knowledge hasincreased. However, many interviewees were occu-pied by questions such as “how the knowledge istransferred into new products and services” and“what does it really mean to be a knowledgeableorganization.” In spite of knowledge managementliterature suggesting positive correlation betweenknowledge capabilities and organizational know-ledge processes (McKenzie and Winkelen, 2004),companies are confused about what they need todo. A possible reason for confusion might be thatcompanies need to find appropriate balancebetween conflicting requirements, which tend tolead organizational attention in opposite directions(McKenzie and Winkelen, 2004). The intervieweespointed out that one of the most significant conflict-ing requirements lies between organizationalefficiency and innovativeness. Organizational efficiencyis based on effective internal knowledge flows,whereas innovativeness rests on knowledge flowsbetween the organization and its environment.Efficiency demands facts and explicit knowledge,whereas innovativeness deals with intuitions and tacitknowledge. Internal knowledge flows can beparalleled to “organizational memory,” which assureoperations by exploiting existing information andprevious experiences, whereas external knowledgeflows represents “organizational sense,” whichenables company to observe weak signals and newideas from the environment (Maula, 2006). Given avariety of knowledge flows, it can be argued that theindispensable perspective for successful transform-ation is how knowledge in its different forms is usedin both internal and external knowledge processes ofthe company. Toomuch focus on internal flows resultsin “blind spots” and decreases company’s ability toinnovate, whereas too much focus on external flowslead to “reinventing the wheel” and organizationalinefficiency.

Operational capabilitiesOperational capabilities refer to company’s abilityto implement strategic plans. Operational capabil-ities increase company’s operational efficiency that

targets optimal results at minimum overall cost. Atits best, combining optimization of operational costsand execution time with increasing revenue andimproving business performance leads to operationalexcellence (Liker, 2004). Operational excellence is anapproach that stresses the need of continual improve-ment. All of the interviewees emphasized that oneimportant aspect of the continual improvement isthat the companies know how they are currentlyperforming. To understand how they are performing,companies need relevant, accurate, and timely infor-mation about processes and performance (Nenadál,2008). All three requirements concerning processand performance information are also in the heart ofthe predictive capability. That is because the predict-ive capability, by its definition, means the company’sability to observe and analyze the internal and exter-nal business events that may affect its near future.Understanding internal and external business eventsand their business impact requires the appropriateprocess performance measurement methodologiesand metrics (e.g., valid Key Performance Indicators)and adequate IT infrastructure (e.g., ERP, CRM). Bydeveloping proactive measurement activities, com-panies increase their capability to detect, anticipate,and manage ongoing business processes.

CONCLUSIONS

Based on literature and interviews, it can beconcluded that a great potentiality of the predictivecapability does exist in the manufacturing industry.However, as mentioned earlier, predictive capabilityis currently just an emerging mode of doing busi-ness. To realize the potential of predictive capability,companies must go through a change process. Thesuccess of the change process, in turn, is dependentupon three critical success factors. The CSFs for thechange toward predictive capability are (i) identify-ing the change drivers; (ii) organizing vision for thechange; and (iii) the capacity to execute change. It isimportant to notice that we consider the causalitybetween the CSFs and the development and imple-mentation of predictive capability as “fragile” inits nature. In other words, the CSFs are necessarybut not a sufficient condition for the conversion tothe predictive capability.The three CSFs were furthermore divided into 11

dimensions. This paper argues that both externaland internal challenges currently drive companiesto the predictive capability. Increasing networking,changing customer needs, rising of the knowledgeeconomy, and development of information technol-ogy were identified as change drivers that createboth pressure and opportunities for observing,analyzing, and optimizing information frominternal and external information sources. Whencombined with strategic, management, knowledge,and operational capabilities, this may result in



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strategic, transactional, and informational benefits(see TableT3 3).

However, in addition to beneficial consequences,there are also unintentional negative outcomes suchas alert overload and power conflicts. An increase ofIT applications amplifies the risk of “overusing” ofthe real-time capability that may in turn overloaddecision makers with too many alerts in much thesame way that e-mails, text messages, etc., havedone (Welke et al., 2007). Furthermore, it is shownthat although an increase in the frequency of feed-back enhances performance as decision makers areable to quickly respond to changes in the environ-ment, there is also a tendency for quick feedbackto bias the decision makers’ information gatheringto the most recent data (Lurie and Swaminathan,2009). As Lurie and Swaminathan further note“the greater the variance in the data, the more likelyit is that more frequent reporting can skew decisionmaking.” This is line with the thoughts of Weickand Ashford (2001) who have argued that informa-tion can have a positive or negative value depend-ing on whether it is a source of pain or pleasure.This “hidden” side of the information may lead tothe situation where people and organizations donot want more information. It is also important toremember that information technology that istechnically and economically feasible can be sociallyinfeasible (Bostrom and Heinen, 1977). This isbecause the technology affects bargaining power bothinside and outside of the organization, disrupt socialprocesses, and violate social taboos (Markus, 1983).In predictive business, there is, for example, the issueof too much transparency, which may cause a nega-tive experience of “big brother” effect. In otherwords,the intentions are positive, but the consequences maybe negative.

Finally, it is important to notice that the frame-work presented in this paper is indicative in nature.Based on the interviews carried out, there are sig-nals suggesting that there is willingness among thecompanies to tackle the challenges and to start thechange toward developing and implementing a pre-dictive capability. However, the Finnish manufac-turing companies are currently only at the stage oforienting toward this new managerial paradigm,so there is still a long road ahead to implementation

and to the realization of any benefits. Essentially,this study can be understood as a “springboard”for further empirical research. Further researchshould be carried out to validate the framework.

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Table 3 The framework for successful change to enable the development and implementation of a predictive capability

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Increasing networking Strategic benefits Strategic capabilities A solid basis for successful change toenable development and implemen-tation of a predictive capability

Changing customerneeds

Transactional benefits Management capabilities

Rising of the knowledgeeconomy

Informational benefits Management capabilities

Development of theinformation technology

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