deliberate learning mechanisms for stimulating strategic innovation … · ·...

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Deliberate LearningMechanisms for StimulatingStrategic Innovation Capacity

Liselore Berghman, Paul Matthyssens, Sandra Streukens andKoen Vandenbempt

Prevailing studies have demonstrated the importance of learning for an organization’sinnovation outcome. Both strategic management and strategic marketing theories havestressed the importance of continuously reinventing business models and creating newcustomer value. We extend these views by focusing on the impact of deliberate learningmechanisms on an organization’s strategic innovation capacity. To this end, we re-interpretabsorptive capacity through a cognition lens. A PLS analysis on survey data suggests thatstrategic innovation capacity is strengthened when managers deliberately install specificlearning mechanisms on the three dimensions of absorptive capacity: knowledge recog-nition, assimilation and exploitation. Results complement existing research by indicatingthe importance of deliberate action when trying to break through existing industrypractices.� 2012 Elsevier Ltd. All rights reserved.

IntroductionOrganizational processes for information processing and knowledge management (Easterby-Smithand Prieto, 2007; Szulanski, 1996) have been studied in a substantial body of work, both in the dy-namic (Eisenhardt and Martin, 2000; Teece et al., 1997; Wilden et al., 2012) and knowledge-basedview of the firm (Grant, 1996). Especially, researchers in the fields of innovation and strategic mar-keting have illuminated the competitive value of an organization’s outside-in information process-ing capabilities (Arbussa and Coenders, 2007; Baker and Sinkula, 2007; Day, 2002). Notsurprisingly, absorptive capacity (Cohen and Levinthal, 1990) has grown into an intensively studied

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concept (Camison and For�es, 2010; Todorova and Durisin, 2007; Volberda et al., 2010), linking in-ternal organization processes to innovation outcomes. Notwithstanding its contributions, researchon absorptive capacity has so far remained predominantly confined to innovations of a technolog-ical kind (Beckeikh et al., 2006, Lane et al., 2006; Rothaermel and Alexandre, 2009) and severalresearch gaps remain (Volberda et al., 2010).

Still, the study of non-technological types of innovation is growing steadily (Birkinshaw et al.,2008). In particular, the concept of “strategic innovation” has caught the eye of academia andbusiness. This type of innovation follows a Schumpeterian perspective, focusing on innovationof the business model and breaking with industry rules of competition (e.g., Christensen,1997; Kim and Mauborgne, 1999; Markides, 1999, 2006; Gunther McGrath, 2010; Teece, 2010;Yu and Hang, 2010). As companies creating these kinds of innovations show high revenue andprofit growth (Kim and Mauborgne, 1999) a deeper understanding of the organizational capabil-ities driving strategic innovation would be highly relevant. Yet, given its nascent status, strategicinnovation research shows a lack of validated and quantitative studies (Govindarajan andKopalle, 2006). Moreover, insights on organizational capabilities for information processing inthe context of technological innovation may prove difficult to transfer to the field of strategic in-novation as different types of innovation may require different information search and processingstrategies (Sidhu et al., 2007) and different managerial interventions (Abernathy and Clark,1985).

The purpose of this paper is hence to explore the deliberate mechanisms that firms, seeking tostimulate their strategic innovation, can establish to affect the different dimensions of absorptivecapacity. Cohen and Levinthal (1990) and Kim (1998) already argued that the development of ab-sorptive capacity requires dedicated managerial effort. In the context of fundamentally new strategicmoves, deliberate learning is considered more effective than semi-automatic experience accumula-tion (Zollo and Singh, 2004), even in domains where the firm lacks experience (Lenox and King,2004). However, insights into the specific organizational mechanisms that enhance strategic learn-ing is still limited (Barkema and Schijven, 2008). Furthermore, broadening absorptive capacity tothe area of strategic innovation offers the opportunity to make the concept more reconcilable withthe issue of path-breaking change (Karim and Mitchell, 2004).

Building on a literature review and field data, we re-interpret absorptive capacity through a cog-nition lens and argue that deliberate learning mechanisms can affect specific practices for recogni-tion, assimilation and exploitation of new ways to create customer value. These ideas areempirically tested on a sample of Dutch industrial firms by means of variance-based structuralequations modeling (PLS Path Modeling) (Hair et al., 2011a,b). Our results illustrate which delib-erate mechanisms affect the different dimensions of absorptive capacity in such a way that the or-ganization’s strategic innovation capacity is strengthened. PLS is a powerful method to bring this tothe fore as it emphasizes prediction of the dependent variables (Hair et al., 2011b; Reinartz et al.,2009), in this case the strategic innovation capacity.

This study contributes to the literature in different ways. First, our findings demonstrate thevalue of intentionally stimulating absorptive capacity for strategic innovation. A particular con-tribution to the strategic management literature lies in the integration of the absorptive capacityconcept with insights from sensemaking research, bringing the importance of shared mentalmodels in an organizational information processing capability to the fore (Lane et al., 2006;Todorova and Durisin, 2007), and questioning the strong path-dependent character of absorp-tive capacity (Cohen and Levinthal, 1990; Liao et al., 2003). In addition, our research adds tothe dynamic resource based view (Teece et al., 1997) as it further evidences how firms developcompetence not only by accumulating experience but also by investing time and effort in activ-ities that require a more cognitive effort (Kale and Singh, 2007; Zollo and Winter, 2002). Con-sidering absorptive capacity as a dynamic capability (Narasimhan et al., 2006; Zahra and George,2002), this study sheds light on concrete second-order mechanisms for dynamic capability cre-ation (Danneels, 2008). Finally, our study contributes to the field of innovation management andmarketing management as it illuminates micro-level implications of strategic innovation capacity

2 Learning mechanisms for stimulating strategic innovation capacity



creation (Rajagopalan and Spreitzer, 1996) by showing clearly that new value creation requiresexperience in specific “market driving” capabilities beyond the traditional marketing toolkit.As such, it offers managers concrete tools for stimulating proactive market approaches.

Our account begins with a conceptual integration of strategic innovation (further referred to asSI) and absorptive capacity (further referred to as ACAP). Combining existing literature with qual-itative findings, we then develop hypotheses regarding different deliberate learning mechanisms forthe three dimensions of ACAP: recognition, assimilation and exploitation. After explaining thequantitative research design and method, we will present and discuss the findings of the hypothesistesting. The paper concludes with theoretical and managerial implications and suggestions for fu-ture research.

Theoretical background

Strategic innovation and new value creationOrganizations’ successful growth strategies in mature industries have raised scholars’ interest in“disruptive innovation” (e.g., Christensen, 1997; Yu and Hang, 2010). Furthermore, some success-ful disruptive innovators succeeded in creating new markets and countering herd behavior withoutany technological advancement (e.g., Birkinshaw et al., 2011; Greenwood and Suddaby, 2006; Kimand Mauborgne, 1999), such as Zopa.com’s very successful introduction of the “eBay” principle tothe financial services industry, or Akzo Nobel’s introduction of “Alacar” (a drive-in formula forsmall car damages) in personal car repair services. Researchers were therefore spurred to extendthe original technological meaning of disruptiveness into a more fine-grained definition (e.g.,Schmidt and Druehl, 2008). Disruptiveness has become defined in terms of new business models(Markides, 2006; Chesbrough, 2010), emphasizing the market-based dimension of the innovation(Danneels, 2003; Govindarajan and Kopalle, 2006). This type of disruptive innovation has gone un-der the name of “strategic innovation” in the strategic management field (Anderson and Markides,2007; Govindarajan and Trimble, 2004, 2005; Markides and Charitou, 2004), and “value innova-tion” in strategic marketing (Matthyssens et al., 2008; Midgley, 2009; Sosna et al., 2010).

SI means that firms deviate from, or even actively alter, the industry rules of the game (Baden-Fuller, 1995; Govindarajan and Euchner, 2010; Govindarajan and Trimble, 2004; Markides, 1999;Miller and Chen, 1996). Yet, deviating from the rules of the game in an industry (Spender,1989) is unlikely to produce economic rents unless a new and superior value proposition is offeredas well (Jaworski et al., 2000; Slater and Narver, 2000; Sirmon et al., 2007). Examples of the latterare Cirque du Soleil’s reinvention of the circus, the exclusive “Nespresso experience” concept in thecoffee market, and GE Aircraft’s selling of flight hours rather than the selling of jet engines. Hence,in this paper we define strategic innovation as a deviation of the industry rules of the game in order tooffer new and substantially superior customer value.

In order to avoid a total reliance on “lucky shots” and/or to better spread risks, authors haveproposed the use of portfolios of strategic innovation initiatives (Govindarajan and Gupta, 2001;Govindarajan and Trimble, 2004, 2005). Furthermore, in terms of sustained competitive advantage,studies have demonstrated the beneficial effects of a continuous innovation cycle, resting on differ-ent initiatives over time (Larsen et al., 2002). Recently, also research on business models has stressedthe importance of continuous variation and innovation (Baden-Fuller and Morgan, 2010). There-fore, we focus on strategic innovation capacity; i.e., an organization’s capacity to create strategic in-novation initiatives in a systematic way.

The study is focusing more on organizational aspects than on strategic market aspects: The exactcontent of strategic innovations initiatives as such are not targeted, but rather the processes andmechanisms that enable companies to continually create such initiatives. When using the term“strategic innovators,” we actually refer to organizations that have a higher than average strategicinnovation capacity in their industry. It parallels earlier efforts to measure “entrepreneurial procliv-ity” (Matsuno et al., 2002)

Long Range Planning, vol -- 2012 3



Absorptive capacity and strategic innovationCohen and Levinthal’s (1990) original definition of absorptive capacity has inspired scholars fromdiverse management fields to use and re-conceptualize the concept (Easterby-Smith et al., 2008;Lane et al., 2006). The multidimensional construct is defined as “a set of organizational routinesand processes by which firms recognize, assimilate, and exploit new external knowledge to createnew knowledge and/or commercial outputs” (Lane et al., 2006; Zahra and George, 2002). In linewith Cohen and Levinthal (1990) and Todorova and Durisin (2007) we name the first dimensionrecognition capability, which is defined as a firm’s processes aimed at identifying and acquiring newvaluable external knowledge. The second dimension assimilation capability covers a firm’s processesaimed at interpreting and understanding the acquired external knowledge (Zahra and George,2002). Assimilation combines new with existing knowledge emphasizing internal knowledge sharingand changing collective mental models (Lane et al., 2006). Finally, exploitation capability consists ofstructural, systemic and procedural mechanisms to harvest and incorporate assimilated knowledgeinto existing operations, so that exploitation can be sustained over a longer period of time (Laneet al., 2006, Zahra and George, 2002). Exploitation denotes a firm’s capacity to improve, expandand use routines and competencies to “create something new” (Flatten et al., 2011).

Conceptualizing ACAP in this way enables us to make the theoretical relationship with SI capac-ity explicit. First, ACAP is conceptually and empirically associated with proactive strategic behavior.For instance, Cohen and Levinthal (1990, 1994) assert that firms with higher levels of absorptivecapacity tend to be more proactive, especially in high-velocity environments (Tsai, 2001; Zahraand Hayton, 2008; Zahra and George, 2002). Consequently, the concept of absorptive capacity isalso related to the creation of new knowledge configurations (Henderson and Clark, 1990) andcombinative capabilities (Jansen et al., 2005; Volberda et al., 2010). Second, the central role of ex-ternal knowledge absorption and outside-in learning processes inherent to absorptive capacity(Arbussa and Coenders, 2007; Lane and Lubatkin, 1998; Spithoven et al., 2010) is also stressedin the literature on strategic innovation (Almeida et al., 2003; Christensen et al., 1998). Deviatingfrom the industry rules of the game may originate from absorbing and commercially exploiting ex-ternal knowledge from non-traditional domains (Van den Bosch et al., 1999; Danneels, 2008; Liaoet al., 2003). Pitt (1998) even asserts that the study of knowledge creation and exploitation could inthis sense be considered as one of the most fruitful ways to map strategic innovation.

Learning mechanisms for strategic innovationThe value that sensemaking theory may have for absorptive capacity theory is manifest, as the fun-damental absorptive capacity dimensions of recognition, assimilation and exploitation closely re-semble the strategic sensemaking cycle of cognition-action processes of environmental scanning,interpretation and associated action (Gioia and Chittipeddi, 1991). Although research has raisedits interest in the cognitive implications of absorptive capacity only recently (e.g., Volberdaet al., 2010), Cohen and Levinthal (1990) themselves originally grounded the absorptive capacityconcept in theory on socio-cognitive structures.

Taken-for-granted industry rules of the game might affect firms’ absorptive capacity sincethese rules also shape on an organizational level how information is noticed, how it is inter-preted, and what kind of action is to be taken (Barr et al., 1992; Daft and Weick, 1984;Spender, 1989). “Dominant logics” (Prahalad and Bettis, 1995) influence how a firm recognizes,assimilates and exploits environmental stimuli (Bettis and Wong, 2003; Sinkula, 2002). Domi-nant logics may help to increase efficiency but may also become toxic “blinders” (Prahalad,2004). They may create information filters that limit search spaces (Bettis and Wong, 2003)and hence distract attention from emerging opportunities (Miller, 1993; Prahalad, 2004) andradically new strategies (von Krogh et al., 2000). Dominant logics may also create lenses that de-termine the interpretation systems (von Krogh et al., 2000). Because of cognitive structures as-similation tends to be local and “close in” to previous activities; new cues may be wronglyinterpreted. Finally, the actions a firm chooses to deal with new cues may be inappropriateand limited to the organization’s “accepted repertoire” (Sinkula, 2002). In addition, these actions




further reinforce this logic by providing in turn new information that can be interpreted in lightof the existing logic (Barr et al., 1992).

Therefore we argue that learning mechanisms might be put in place to break down these infor-mation filters (for identifying and obtaining information); lenses (for interpreting and understand-ing information); and action repertoires (for incorporating new knowledge into existing operationsin order to create something new). Learning mechanisms stimulate a more “open-minded” recog-nition, assimilation and exploitation of external knowledge. First, learning mechanisms that fosterthe capacity to recognize new opportunities and options (O’Connor and Rice, 2001) is expected tostimulate SI capacity as it enables deviations from existing heuristics in the organizational knowl-edge base (Ahuja and Katila, 2004). Acquisition of non-local (Nooteboom et al., 2007) and periph-eral information (Day and Schoemaker, 2004) may help to find new market opportunities and thusfacilitate entering new strategic domains (Danneels, 2003). Second, learning mechanisms that fosterassimilation may have a beneficial effect on SI capacity. Scholars in the strategic innovation fieldhave emphasized the importance of an inquiring and non-dogmatic collective mindset (e.g.,Baden-Fuller and Stopford, 1994; Hamel and V€alikangas, 2003). They argued further that it isnot so much the possession of market knowledge, but the shared, critical interpretation of itthat leads to an increased innovation effort (De Luca and Atuahene-Gina, 2007; Marinova,2004). In the SI literature, assimilation processes, such as cross-functional discussions on the firm’sassumptions regarding its customers, market and marketing approach, have hence been promoted(Markides, 2006; Tripsas and Gavetti, 2000).

Third, learning mechanisms that stimulate exploitation can be expected to foster SI capacity, withtheir emphasis on implementation and knowledge-use practices (Akg€un et al., 2007). New knowl-edge should eventually materialize in the execution of new business concepts (Tuominen et al.,2004). Exploitation consists of efficiently harvesting and incorporating assimilated knowledgeinto existing operations (Zahra and George, 2002; Lane et al., 2006), which might require organi-zational adaptations in procedures (Teece et al., 1997), ways of working (Deneault and Gatignon,2000), and skills (Zander and Zander, 2005).

Deliberate learning mechanisms for strategic innovationScholars have asserted that organizations generally learn in two ways: in a quasi-automatic, uncon-scious way of experience accumulation, or in a more deliberate way (Arthur and Huntley, 2005).The first way of experience accumulation is believed more and more to be a necessary but insuffi-cient condition for organizational learning and dynamic capability creation (Romme et al., 2010).Strategic settings with high levels of causal ambiguity and complexity require instead interventionsof a more deliberate kind (Barkema and Schijven, 2008). Researchers have recently demonstratedthe value of the latter in diverse strategic contexts, such as hospitals (Nembhard and Tucker,2011), industrial plants (Arthur and Huntley, 2005), and in the context of mergers and acquisitions(Barkema and Schijven, 2008). This “deliberate learning” (Zollo and Winter, 2002) which has alsobeen called “induced learning” (Nembhard and Tucker, 2011), or “planned learning” (Levy, 1965),stresses more the cognitive and conscious aspects of the learning process; the aim is to enhance theunderstanding of the causalities between practices and their performance implications. Managersintervene in the learning process, designing and implementing specific mechanisms that affectthe ability, motivation, and experience-based learning of organization members in diverse activitiesfor acquiring, codifying or transferring knowledge (Arthur and Huntley, 2005). Popular examplesinclude experiments, training or suggestion programs (Nembhard and Tucker, 2011).

Already in their seminal 1990-article, Cohen and Levinthal argued that absorptive capacity willnot gradually arise as a natural “byproduct” of the innovation process, but that its creation will re-quire the dedication of explicit effort. Therefore, we argue that three categories of deliberate learningmechanisms might prove especially beneficial to foster SI capacity. Deliberate mechanisms for recog-nition target the “filter”; deliberate mechanisms for assimilation target the “lens”; and deliberatemechanisms for exploitation target the “action repertoires” of mental schemata (Prahalad andBettis, 1995).




Considering absorptive capacity as a dynamic capability (Narasimhan et al., 2006; Volberda et al.,2010), these deliberate mechanisms may in fact be considered as second-order learning mechanisms(Danneels, 2008; Winter, 2003) that create and modify dynamic capabilities, and thus keep themfrom perishing over time (Eisenhardt and Martin, 2000; Schrey€ogg and Kliesch-Eberl, 2007). Theidea is to install mechanisms of a higher order that modify the underlying operating routines forabsorptive capacity (Zollo and Winter, 2002). We argue that deliberate mechanisms for recognition,assimilation and exploitation may foster the creation of strategic innovation capacity through facil-itating a change in underlying customer or market knowledge processing capabilities, in such a waythat information filters, lenses and action repertoires are redirected. For example, a routine for per-forming customer research would be a dynamic capability routine, as it may provide opportunitiesfor innovation (Eisenhardt and Martin, 2000; Winter, 2003). Yet, a deliberate learning mechanismfor recognition pertains to a still deeper level, and tries to deliberately affect the specific way cus-tomer research is being performed in an effort to capture “market driving” information.

In the following paragraph, we explain each category of learning mechanisms in greater detailand hypothesize their relationship with strategic innovation capacity.

HypothesesIn order to start our theorizing “sufficiently close to the phenomenon under study” (Shepard andSutcliffe, 2011), we deemed it necessary to enrich our conceptual arguments with qualitative data(Tashakkori and Teddlie, 1998). To discover the relevant aspects that deliberate learning mecha-nisms should target in each of the three ACAP dimensions, we simultaneously applied a literaturestudy (see Data and Methods section, left hand side Figure 2) and a qualitative study (right handside Figure 2). We adopted a research strategy as suggested in Flatten et al. (2011). Concerning theliterature study we reviewed existing research on ACAP but also on SI and on related researchstreams (e.g., research on market driving). Based on the definitions of the three ACAP dimensions

Figure 1. Theoretical model




Figure 2. Research design

(see Theoretical Background section) three authors of this paper individually assigned crucial as-pects in this literature to the three ACAP dimensions, then the three classifications were jointly dis-cussed and agreed upon. (For example, consulting innovative customers for new ideas was relatedto identifying and acquiring new external knowledge, and can thus be assigned to the recognitiondimension, whereas adapting the organizational structure was considered an aspect of exploitation,since structure entails “structural, systemic and procedural mechanisms” as covered by the exploi-tation dimension). In parallel, we performed a qualitative study with the same goal (see Data andMethods section, Figure 2). We first interviewed 61 managers (responsible for firms’ market strat-egy) in various Dutch industrial sectors to identify and select companies (or business units formulti-unit firms) with a high strategic innovation capacity. In these firms, we in turn interviewed52 managers who were highly involved in the setup of strategic innovation initiatives, and asked forcritical aspects in the process. Additional desk and expert research (e.g., consultancy reports aboutthe company) as well as interviews with customers were used for data triangulation. The qualitativeanalyses illustrate the relevance of establishing learning mechanisms for recognition, assimilationand exploitation. Furthermore, across all companies and industries, a pattern arose where similaraspects in the absorptive capacity dimensions were deliberately stimulated. Traditional marketingresearch procedures turned out to be supplemented with learning mechanisms to “dig deeper”into trends; to look beyond the usual customer needs research; to critically reflect on deep marketinsight; and to deliberately facilitate and stimulate the use of acquired and assimilated market-driving knowledge.

Even though the literature review and qualitative study were performed separately, they still in-formed each other. As the arrows in the middle of Figure 2 (see Data and Methods section) show weapplied an iterative research process (Danneels, 2002) between data collection and analysis(Eisenhardt, 1989; Eisenhardt and Graebner, 2007), and between theory and empirics (Orton,1997). The resulting crucial elements that are stimulated by deliberate learning mechanisms thusreflect both the literature and qualitative results. The qualitative data, combined with the insights




from the literature enabled us to develop hypotheses on specific deliberate mechanisms that can beused for each absorptive capacity dimension (see Figure 1).

RecognitionBy influencing the recognition capability, the way new information is sought and “filtered” is af-fected. Scanning an organization’s external environment is considered as the starting point of sense-making (Daft and Weick, 1984; Narayanan et al., 2011). Especially, for firms adopting radical orproactive innovation strategies, capabilities for external knowledge recognition are of critical im-portance (Baker and Sinkula, 2007; Liao et al., 2003). In particular, scanning more remote environ-mental areas helps firms to find new market opportunities, and thus to enter new strategic domains(Danneels, 2003, 2008) and to discover new business models (Gunther McGrath, 2010). Our liter-ature study and the qualitative data reveal that deliberate mechanisms for recognition essentiallystimulate insight into the following elements:

� future customer needs� industry tendencies� deep customer needs� general environmental information (macro-tendencies, regulation, etc.)� innovative customers� other industries� end customer� non-customers

The focus on future customer needs (Kumar et al., 2000; Slater and Narver, 1998) and industrytendencies match strategic innovation researchers’ emphasis on the development of industry fore-sight (Brown and Eisenhardt, 1997; Markides, 1999). In line with the more traditional business-to-business marketing literature (Day, 1999; Varadarajan and Jayachandran, 1999) strategic innovatorsstress the need to deeply study the most innovative existing customers and consult them for ideas.Furthermore, strategic innovators indicate the importance of gaining insights into the end-customer, non-customers (Kim and Mauborgne, 1999; Markides, 1999) and other inspiring indus-tries (Gavetti and Rivkin, 2005; Markides, 1999). For example, an energy services providerdeveloped a detailed scheme of all its non-customers, categorized them and developed a new busi-ness model to enhance value to them. These findings show the importance of non-local search(Lane et al., 2006; Nooteboom et al., 2007) and peripheral vision (Day and Schoemaker, 2004)for strategic innovation capacity. Strategic agility, a fundamental determinant of business model re-newal and transformation, sets off with “strategic sensitivity” which implies recognition elementssuch as hearing the voice of the periphery and sharpening foresight (Doz and Kosonen, 2010).As such we posit:

Hypothesis 1: Deliberate learning mechanisms for recognition are positively related to a firm’sstrategic innovation capacity by fostering insights into future customer needs, industrytendencies, deep customer insight, general environmental information, innovative customers,other industries, the end customer and non-customers.

AssimilationLearning mechanisms for assimilation influence the “lens” through which interpretation may beconstrained (Prahalad and Bettis, 1995). Interviewees mention the deliberate stimulation of:

� critical reflections on customers� critical reflections on markets� critical reflections on the marketing approach




� keeping alive past critical reflections on customers and markets� sharing critical reflections on customers and markets� filing critical reflections on customers and markets

The qualitative study reflects the sensemaking and strategic innovation literature quite well. Stra-tegic innovators stress the need to broach (cross-functional) discussions about customers, the mar-ket, and the business unit’s marketing approach (De Luca and Atuahene-Gina, 2007; Doz andKosonen, 2010; Markides, 2004; Tripsas and Gavetti, 2000). New interpretations may uncovernew applications of existing knowledge (Marsh and Stock, 2008). Pluralism of ideas is triggeredby knowledge integration mechanisms in order to stimulate cross-functional and cross-hierarchical dialogue (Hargadon and Fanelli, 2002; Thomas et al., 2001; Weick and Roberts,1993; Wells, 2008). Especially when external knowledge is path-breaking and does not fit with ex-isting cognitive schemes, tools for assimilation are needed (Todorova and Durisin, 2007). Discus-sion of multiple hypotheses, disconfirming questions, dissenting viewpoints (Bartunek et al., 1983),creative interpretation of information, and brainstorming about possibilities (Day and Schoemaker,2004) are identified as being useful in this respect. Explicit effort is dedicated to keeping alive in-sights from previous reflections in order to sharpen insight into causalities (Marsh and Stock, 2008;Zollo and Winter, 2002). Mechanisms stimulate the use of periodical, cross-functional meetingsthat sometimes involve external parties such as customers, specific user groups, partner companies,or university professors. Hence we put forward:

Hypothesis 2: Deliberate learning mechanisms for assimilation are positively related to a firm’sstrategic innovation capacity by stimulating critical reflections on customers, critical reflectionson markets, critical reflections on the marketing approach, keeping alive past critical reflectionson customers and markets, and sharing and filing critical reflections on customers and markets.

ExploitationDeliberate mechanisms for exploitation aim at better harvesting and incorporating newly assimi-lated knowledge into existing operations (Zahra and George, 2002). In contrast to the cognitive as-pects of absorptive capacity present in recognition and assimilation, exploitation capability pertainsto the behavioral component of sensemaking (Weick, 1979) and hence produces a change in oper-ating routines that is required for strategic innovation (Almeida et al., 2003). Baden-Fuller andMorgan (2010) stress the dynamic aspect of business models which leads firms to experiment,change, refine and reinvent their business models.

For real transformation to occur, not only knowledge content should be addressed but alsoknowledge-use practices (Akg€un et al., 2007). Recent research in the field of sensemaking by Naget al. (2007) demonstrated that in processes of strategic change a cognitive change does not auto-matically infer a change in organizational practices as well; “what we know” may differ from “whatwe do”. Their study of the change process at TekMar shows that even though middle managersreadily assimilated new knowledge content into their thinking, they failed to also apply this knowl-edge in the form of new business development procedures. Doz and Kosonen (2010) stress the needfor aligning, integrating and grafting in business model renewal and transformation.

Our interview data also show that learning mechanisms for exploitation primarily stimulate thefollowing areas:

� adapt the organizational structure� support new initiatives, even to the detriment of existing business� adapt procedures� replace skills/competencies� change the way of working� prevent organizational chaos




Almost all initiatives imply an adaptation of the organizational structure (Daft and Weick, 1984),such as the set-up of detached, cross-functional, temporary project teams. In addition, support tonew initiatives seems extremely important (Govindarajan and Trimble, 2005). Other important fac-tors are the stimulation of changes in procedures and ways of working (Deneault and Gatignon,2000; Teece et al., 1997). The accommodation of new or different customer needs results in the for-mation or assimilation of previously unexploited skills (Zander and Zander, 2005). Chaos isavoided as it worsens market credibility and considerably retards implementation and roll-out(Ahuja and Lampert, 2001). Stimulating a strict project-driven approach, the use of implementa-tion blueprints and the set-up of separate units are frequently mentioned as highly valuable toolsin this respect. All of this leads to:

Hypothesis 3: Deliberate learning mechanisms for exploitation are positively related to a firm’sstrategic innovation capacity by stimulating the firm to adapt the organizational structure,support new initiatives even to the detriment of existing business, adapt procedures, replace skills(competencies), change the way of working, and prevent organizational chaos.

Although we hypothesize positive and direct effects of the three categories of learning mechanismsand strategic innovation capacity, additional mediated effects may be expected as well (see the greyarrows in Figure 1). ACAP theory points to the complementarity of the three dimensions (Zahraand George, 2002) and some authors even assert sequentiality (Lane and Lubatkin, 1998). Mediationeffects may also be derived from sensemaking theory (e.g., Thomas et al., 1993), and could be inferredfrom our qualitative study as well. Yet, mediation effects in this study are a little bit less straightfor-ward than one might expect at first sight. Reason for this is the exclusive focus on deliberate learningmechanisms in our study. This implies that even though important (maybe full) mediation may beexpected of recognition and assimilation through exploitation, this does not automatically implythat (full) mediation effects will also exist between the deliberate mechanisms stimulating specificpath-breaking elements in the three dimensions. In other words, in this study we restrict our attentionto what essentially are “flow” variables. Underlying “stock” variables (meaning underlying recogni-tion, assimilation and exploitation capacity as they are) are not taken into account.

Data and methods

Data collection and sample descriptionFigure 2 shows our research design. We followed a sequential qualitative-quantitative design(Tashakkori and Teddlie, 1998). Based on a review of the literature and on an extensive qualitativeresearch we developed our theoretical model (as specified in the Hypotheses section).

We then tested the hypothesized relationships by means of a quantitative study. We extracteda stratified random sample of 2970 companies from the DMCD database, containing more than95% of all Dutch companies. Eight strata were defined: industrial product and service companieswere each classified into four categories of company sizes (number of fulltime equivalents). Weused a telephone pre-qualification method to elicit cooperation for the final Web survey(Couper, 2000). To reduce measurement error, the survey was extensively pretested followingBagozzi’s (1994) guidelines, for example, through an in-depth review by experts, an e-mail pilot.As a result of the pre-testing phase, several items/questions were re-formulated. The final measuresare shown in Appendix. In line with research on strategic innovation (Kim and Mauborgne, 2004)and absorptive capacity (Jansen et al., 2006), the level of analysis was the company or business unit(for multi-unit firms). Respondents were managers in charge of market strategy (marketing man-agers in large companies, CEO in small companies). Although we do acknowledge the potential biasin single-informant self-report studies (Cycyota and Harrison, 2002), we followed extant strategyliterature, which is characterized by the frequent use of self-reports of managers/executives to ex-amine an organization’s (knowledge) processes and strategy (Baker and Sinkula, 2007; Hult et al.,




2005; Joshi and Sharma, 2004). The telephone pre-qualification process led to 816 potential respon-dents of which 188 (or 23% of telephone respondents) actually filled out the web survey. Notwith-standing the use of response inducement techniques, which have proved effective in eitherindustrial samples (Diamantopoulos and Schlegelmilch, 1996; Erdogan and Baker, 2002) or inon-line surveys (Deutskens et al., 2004) response rate remained relatively low. However, this isa “normal” rate for business studies and web surveys (Couper, 2000; Grandcolas et al., 2003;Joshi and Sharma, 2004; Umbach, 2004). We assessed nonresponse bias by comparing the samplecomposition in the different survey stages. Results demonstrated a slight over-representation of verylarge companies (þ500 FTEs) and product companies vis-�a-vis population data. Respondents wereequally spread among up-, mid- and downstream companies. In addition, a comparison of earlyversus late respondents on company demographics and theoretical constructs (Armstrong andOverton, 1977) suggested that a threat of non-response bias could not be discerned on any ofthe variables (p2-tailed > 0.05). The results of Harman’s one-factor test (Podsakoff and Organ,1986) suggested the absence of common method bias.

MeasuresDue to the lack of any validated operationalizations of the constructs we had to develop new mea-sures. We used the literature study and the qualitative findings (Figure 2) to specify the contents ofthe construct before indicators were formulated (Bagozzi, 1994; Rossiter, 2002).

Independent variablesTo operationalize the learning mechanisms of the different dimensions of ACAP we followed theprocedure as shown in Flatten et al. (2011). Measures that we developed for the independent vari-ables were based on accepted definitions of the ACAP dimensions (e.g., Cohen and Levinthal, 1990;Lane et al., 2006; Todorova and Durisin, 2007) and fully built on the crucial aspects in each dimen-sion that we had detected in the literature review and qualitative study (see Hypotheses). Combin-ing a literature review and qualitative study is furthermore considered a valid design for formativemeasurement construction (Reinartz et al., 2004).

Deliberate learning mechanisms for recognition, as perceived by people in charge of market strategy,are deliberately established mechanisms that radically affect an organization’s capacity for identifyingand acquiring new valuable external knowledge. Deliberate learning mechanisms for assimilation aredeliberately established mechanisms that radically affect an organization’s capacity for interpretingand understanding the acquired external knowledge, and deliberate learning mechanisms for exploita-tion are deliberately established mechanisms that radically affect an organization’s capacity for im-proving, expanding and using routines and competencies to “create something new”.

All independent variables were operationalized in a formative specification mode. The appropri-ateness of the formative measurement mode was verified using the guidelines by Fornell andBookstein (1982), Chin (1998), Diamantopoulos and Winklhofer (2001) and Jarvis et al. (2003).The theoretical and qualitative study revealed that deliberate learning mechanisms stimulate specificaspects (e.g., the study of non-customers) but these aspects may be stimulated to a larger or smallerextent dependent on the firm, or even on the kind of initiative. Accordingly, learning mechanismsfor different aspects are not interchangeable (not inter-correlated) by definition. As one well-chosenformative indicator captures each aspect (Rossiter, 2002), removing indicators would alter the con-tents of the constructs (Diamantopoulos and Winklhofer, 2001; Williams et al., 2003). In the con-crete, the constructs deliberate learning mechanisms for recognition, for assimilation and forexploitation consist of respectively 8, 6, and 6 dimensions (derived from the literature and qualita-tive study) that are each captured by one item (Rossiter, 2002). All items measured on five-pointscale with 1: strongly agree e 5, strongly disagree (n/a category added).

For deliberate learning mechanisms for recognition, Bagozzi and Heatherton’s (1994) framework on“partial aggregation models” (the discrete components model) was used for the dimensions generalenvironmental information (R04) and deep customer insight (R03). For environmental informationwe de-cided to do so because it enabled us to use an existing scale (Matsuno et al., 2002: market intelligence




generation). For customer insight, the combination of reflective indicators illustrated the specific con-tents of the dimension in a clearer way. Environmental information was measured by means of three re-flective indicators (see Appendix) and insight was based on four reflective items (see Appendix).

Dependent variableIn line with the continuous innovation cycle (Baden-Fuller and Morgan, 2010; Larsen et al., 2002),that we explained in the theory section our focus is on portfolios of initiatives. Therefore, we focuson “strategic innovation capacity”. We followed Baden-Fuller’s (1995) advice to not relate this in-novation capacity to some absolute standard, but to consider it in relation to the ability of maincompetitors. In this way, emphasis is put on the deviance from industry rules, more than on theinternal deviance from past market strategies. An additional advantage was that comparative ques-tions lead to a higher measurement quality (Andrews, 1984). Strategic innovation capacity, as per-ceived by people in charge of market strategy, is the BU’s (firm’s) capacity, relative to its maincompetitors’ capacity, to systematically create strategic innovation initiatives that entail the creationof new and substantially superior customer value by a redefinition of the business model and/or analteration of the roles and relationships within the selected industries. The construct was measuredby seven reflective indicators (see Appendix), which were all formulated comparatively in relationto main competitors (Baden-Fuller, 1995). The construct is furthermore operationalized as a con-tinuous variable (Jaworski et al., 2000). The lack of any conceptual consensus about the nature andmeasurement of customer value (e.g., Payne and Holt, 2001) made us define customer value inbroad terms at the beginning of the survey, instead of capturing it in a separate measure.

All items of the independent and dependent variables were measured on a five-point scale with 1:strongly agree e 5, strongly disagree (n/a category added).

Control variablesWe controlled for potential homogenization effects of the business unit’s (firm’s) size(e.g., Charitou and Markides, 2003), its supply chain position (e.g., Jacobides and Winter, 2007;Jaworski et al., 2000) and its type (e.g., Nijssen et al., 2006).

Analytical approachWe opted for PLS path modeling over covariance-based SEM to analyze our model for the follow-ing reasons (cf. Hair et al., 2012; Reinartz et al., 2009). First, our model is exploratory in naturerather than confirmatory. Second, we employ both formative and reflective scales. Third, the num-ber of observations is relatively small. All PLS path modeling analyses were performed usingsmartPLS 2.0 (Ringle et al., 2005), using a path weighting scheme.

All control variables, being categorical, were re-coded into formative dummies (k-1 categories)(Falk and Miller, 1992). Given the highly insignificant effects of the control variables we followedthe principle of parsimony and excluded them from all further analyses.

The suggestions put forward by Kristensen and Eskildsen (2010) were followed in dealing with miss-ing values in our data set (<10% of the cases andmissing values appeared to be completely at random).

Bootstrap percentile confidence intervals were constructed to assess whether the relationships inour model are statistically significant. Following Preacher and Hayes (2008), the number of boot-strap samples was set equal to 5000, with each bootstrap sample containing the same number ofobservations as the original sample. Furthermore, we allowed for individual sign changes in thebootstrap procedure (Hair et al., 2012; Henseler et al., 2009).

In case other analytical approaches are employed, this will be mentioned explicitly in the text.

Measurement propertiesIn assessing the psychometric properties of the scales under study it is important to distinguish be-tween formative and reflective scales as this impact the type of properties that are relevant and theway in which these properties are to be assessed (MacKenzie et al., 2005).




For the multiple item constructs employed, strategic innovation capacity is measured using a re-flective scale and all three deliberate learning mechanisms are tapped using a formative measure-ment model. All relevant measurement model estimates are presented in Table 1 below. Theindicator descriptives and intercorrelations are summarized in Table 2.

Preliminary analysesAs mentioned previously, existing reflective measurement items were used to tap two elements ofdeliberate learning mechanism for recognition. Following Baumgartner and Homburg (1996) weaveraged these items and included them as formative indicators (i.e., one formative indicator forgeneral environment information and one indicator for deep customer insight) order to reducemodel complexity. To test whether our data were indeed appropriate for this strategy, we ran a sep-arate confirmatory factor analysis in AMOS (Bagozzi and Heatherton, 1994), which showed a goodmodel fit (inc. good alternative fit indices).

Psychometric properties of reflective measurement scaleInspection of the eigenvalues of the construct’s inter-item correlation matrix (l1 ¼ 3.85; l2 ¼ 0.88)provides support for the construct’s unidimensionality (Karlis et al., 2003; Tenenhaus et al., 2005).The construct’s composite reliability statistic equals 0.88 which well exceeds the recommended cut-

Table 1. Loadings and weights of the measurement model.

Construct Item Weight Bootstrap percentile CI

Deliberate learning mechanism

for recognition

R01 0.18 [0.02; 0.34]

R02 0.18 [0.01; 0.36] significant at a ¼ 0.10

R03 0.35 [0.18; 0.52]

R04 ns



R07 ns

R08 0.32 [0.13; 0.51]


for assimilation

A01 0.29 [0.11; 0.46]

A02 0.15 [0.01; 0.29] significant at a ¼ 0.10

A03 0.27 [0.05; 0.51]

A04 0.33 [0.17; 0.49]

A05 0.35 [0.12; 0.57]

A06 ns


for exploitation

E01 0.44 [0.62; 0.84]

E02 ns

E03 0.24 [0.06; 0.44]

E04 0.26 [0.08; 0.44]

E05 0.26 [0.05; 0.46]

E06 0.32 [0.10; 0.52]

Strategic innovation capacity S01 0.67 [0.54; 0.77]

S02 0.66 [0.51; 0.76]

S03 0.82 [0.82; 0.86]

S04 0.79 [0.79; 0.84]

S05 0.73 [0.73; 0.80]

S06 0.73 [0.73; 0.80]

S07 0.64 [0.64; 0.74]




Table 2. Descriptives and intercorrelations indicator variables.

M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

1 R01 3.02 1.02 1

2 R02 2.97 1.23 .40 1

3 R03 2.99 .97 .23 .47 1

4 R04 2.93 .83 .36 .55 .49 1

5 R05 3.69 .99 .28 .26 .15 .38 1

6 R06 2.57 1.19 .26 .26 .28 .38 .32 1

7 R07 3.22 1.10 .31 .43 .33 .60 .27 .37 1

8 R08 2.55 .99 .30 .52 .44 .65 .23 .30 .49 1

9 A01 3.13 1.02 .28 .28 .03 .27 .12 .15 .26 .14 1

10 A02 3.31 .99 .27 .16 .08 .39 .37 .16 .36 .37 .20 1

11 A03 2.54 .97 .24 .42 .31 .51 .27 .30 .42 .48 .16 .36 1

12 A04 3.41 .84 .28 .28 .13 .08 .35 .34 .09 .28 .17 .11 .17 1

13 A05 2.72 .99 .21 .21 .45 .35 .54 .33 .23 .32 .47 .23 .65 .32 1

14 A06 2.88 1.20 .10 .10 .29 .24 .34 .22 .14 .40 .42 .19 .49 .11 .40 1

15 E01 2.98 1.14 .39 .39 .41 .30 .35 .29 .14 .31 .37 .20 .47 .30 .44 .24 1

16 E02 2.83 1.10 .32 .32 .22 .15 .33 .18 .18 .26 .29 .21 .21 .22 .29 .16 .38 1

17 E03 3.49 .99 .26 .26 .13 .15 .22 .18 .14 .21 .22 .31 .15 .11 .18 .19 .11 .24 1

18 E04 2.85 .95 .20 .20 .28 .33 .27 .25 .17 .20 .26 .03 .19 .03 .19 .08 .14 .26 .05 1

19 E05 3.30 1.03 .29 .39 .19 .10 .27 .30 .08 .16 .24 .23 .25 .33 .28 .17 .37 .36 .42 .01 1

20 E06 2.86 .97 .31 .31 .32 .27 .47 .26 .21 .29 .41 .25 .36 .35 .46 .30 .53 .47 .25 .15 .39 1

21 S01 3.11 1.06 .01 .01 .24 .13 .23 .32 .25 .27 .20 .23 .13 .24 .30 .20 .13 .07 .13 .07 .11 .13 1

22 S02 2.95 .99 .09 .09 .31 .20 .25 .30 .27 .33 .25 .25 .15 .27 .31 .26 .14 .15 .25 .13 .16 .18 .71 1

23 S03 3.31 .96 .15 .25 .08 .29 .31 .09 .33 .19 .30 .16 .22 .24 .30 .20 .26 .17 .23 .14 .25 .26 .55 .47 1

24 S04 3.10 .91 .26 .42 .23 .48 .31 .27 .33 .37 .29 .24 .32 .30 .43 .28 .45 .32 .31 .18 .34 .45 .43 .38 .52 1

25 S05 3.13 .93 .13 .23 .17 .31 .30 .14 .28 .17 .25 .18 .29 .30 .35 .25 .32 .27 .18 .13 .33 .29 .56 .45 .51 .60 1

26 S06 3.21 .87 .14 .21 .07 .24 .35 .09 .23 .17 .20 .19 .20 .24 .25 .14 .42 .19 .12 .16 .23 .23 .38 .35 .54 .48 .54 1

27 S07 3.48 .85 .15 .20 .07 .24 .32 .23 .19 .29 .24 .24 .33 .19 .36 .23 .31 .12 .28 .18 .33 .22 .41 .32 .48 .45 .43 .42 1

Note: R01eR08, A01eA06, and E01eE06 denote the indicator variables for the deliberate learning processes of respectively recognition, assimilation, and exploitation. S01eS07 are theindicators used to tap strategic innovation capacity.

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off level of 0.70. Within-method convergent validity is indicated by the magnitude and statisticalsignificance of the construct indicators as well as by the amount of average variance extracted(0.52). Finally, discriminant validity is evidenced as the square root of the average variance ex-tracted (i.e., 0.79) of strategic innovation capacity exceeds its correlation with the three deliberatelearning mechanisms (i.e., correlation coefficients with respectively recognition, assimilation, andexploitation are 0.47, 0.50, and 0.48).

Psychometric properties of formatively specified constructsAnalysis revealed that multicollinearity does not play a role in the formative measurement modelsat hand as all VIF values were below the recommended cut-off level of 5 suggested by Hair et al.(2011b). In particular, the maximum VIF values for the formative constructs were 2.51, 1.99,and 1.63 for, respectively, the deliberate learning mechanisms of recognition, assimilation andexploitation.

To evaluate the performance of the formative measurement models used in our study, the indi-cator weights and their statistical significance are the statistics of interest (Hair et al., 2012). Fromthe figures presented in Table 1 we can conclude that our formative measurement models performwell, with exception of two items for the deliberate learning mechanism of recognition (i.e., itemsR04 and R07) and one item for both the deliberate learning mechanism of exploitation (i.e., itemE02) and the deliberate learning mechanism of assimilation (i.e., item A06). In line with Bollen andLennox (1991) and Diamantopoulos and Winklhofer (2001) these items will be retained in theremainder of the estimation procedure, as the item resulted from an extensive qualitative study(see Figure 2), and omitting a formative indicator seriously would comprise the validity of the scale.

Results: structural model

Model performanceModel performance was assessed as follows. First, bootstrapped R2 values were constructed follow-ing Tenenhaus et al. (2005). Subsequently, we constructed the accompanying percentile bootstrapconfidence intervals (cf. Ohtani, 2000). Second, we ran a blindfolding procedure to determine thecross-validated redundancy index for strategic innovation capacity.

Overall, our model shows a good fit to the data as evidenced by the magnitude and confidenceintervals of the bootstrapped R2 values for the endogenous constructs. Formally, we can concludethat all R2 values are statistically different from zero as none of the percentile confidence intervalscontains the value of zero. In particular, we see that all R2 values exceed the cut-off level of 0.19(Chin, 1998). Even though a strict application of Hair et al.’s (2011b) rules of thumb for marketingstudies would suggest a weak-moderate R2 value, these values should always be interpreted in viewof their research context (Hair et al., 2011b, 2012). If deliberate learning mechanisms can explaina variance of 0.36 in innovation capacity on a BU/firm level this can be considered as high, given thepotential effects of so many other internal (e.g., organizational culture) and external (e.g., innova-tion appropriability regime in sector) factors. A similar conclusion is reached when this value iscompared to other innovation studies in prestigious journals (e.g., Cui and O’Connor, 2012;Zhou et al., 2005). The bootstrap R2 values as well as the accompanying bootstrap percentile con-fidence intervals are presented in Table 3 below. The Stone-Geiser Q2 for strategic innovation ca-pacity is larger than 0 (Q2 ¼ 0.18) and is therefore also indicative of an acceptable modelperformance (Henseler et al., 2009).

Interpretation structural model coefficientsTable 3 below summarizes all relevant structural model coefficients as well as the bootstrap percen-tile confidence intervals. Furthermore, we report the f2 effect sizes for all relevant and statisticallysignificant relationships.

Regarding the three categories of mechanisms, we find that strategic innovation capacity is directlyand positively influenced by learning mechanisms for assimilation (b ¼ 0.26; CI.95: [0.10; 0.43]) and




Table 3. Structural model coefficients.

Dependent construct Independent

construct

Coefficient Effect size f2 Bootstrap percentile

confidence interval (95%)

SICAP

(R2 ¼ 0.36 CI95: [0.30; 0.41])

RECOG e e

ASSIM 0.26 0.22 (Moderate) [0.10; 0.43]

EXPLO 0.24 0.19 (Moderate) [0.06; 0.42]

ASSIM

(R2 ¼ 0.48 [0.42; 0.53])

RECOG 0.70 Not applicable [0.61; 0.77]

EXPLO

(R2 ¼ 0.47 [0.42; 0.52])

RECOG 0.37 0.45 (Strong) [0.19; 0.54]

ASSIM 0.38 0.44 (Strong) [0.21; 0.56]

Note: In parentheses we mentioned the interpretation of the effect sizes according to Cohen et al. (2003).

learning mechanisms for exploitation (b ¼ 0.24; CI.95: [0.06; 0.42]). Thus, hypotheses H2 and H3 aresupported by the data. Medium effect sizes are found. There is however no empirical evidence fora direct relationship between strategic innovation capacity and learning mechanism for recognition.Therefore, hypothesis H1 is not supported by the data.

Analysis also reveals that significant relationships exist among the three learning mechanisms.The learning mechanisms for assimilation are positively influenced by the learning mechanism for rec-ognition (b ¼ 0.70; CI.95: [0.61; 0.77]). In turn, the learning mechanisms for exploitation are a func-tion of both learning mechanisms for assimilation (b ¼ 0.38; CI.95: [0.21; 0.56]) and the learningmechanism for recognition (b ¼ 0.37; CI.95: [0.19; 0.54]).

To fully understand the pattern of relationships among the learning mechanisms and strategicinnovation capacity, a formal mediation test needs to be conducted. Following the procedure putforward by Sattler et al. (2010), analysis shows that the effect of learning mechanism for recognitionis fully mediated by learning mechanisms for assimilation (b ¼ 0.18; CI.95: [0.07; 0.31]) and learningmechanisms for exploitation (b ¼ 0.09; CI.90: [0.02; 0.18]). In addition to the statistically significantdirect effect of learning mechanisms for assimilation on strategic innovation capacity mentionedabove analysis points out that there is also an indirect effect via learning mechanisms for exploitation(b ¼ 0.09; CI.95: [0.02; 0.19]). Thus, the effect of learning mechanisms for assimilation on strategicinnovation capacity is partially mediated by the learning mechanisms for exploitation.

Finally, the empirical results show that all three deliberate learning mechanisms have a statisticallysignificant overall (i.e., total) effect on strategic innovation capacity. In order of decreasing impact,the total effect are as follows: learning mechanisms for recognition b ¼ 0.52 (CI.95: [0.41; 0.63]),learning mechanisms for assimilation b ¼ 0.35 (CI.95: [0.18; 0.52]), and learning mechanisms for ex-ploitation b ¼ 0.24 (CI.95: [0.06; 0.42]).

Importance-performance analysis1

As all exogenous constructs in our model are tapped using formative indicators we are in the po-sition to determine the impact of each separate indicator on the focal endogenous construct stra-tegic innovation capacity. Together with the performance on each indicator this leads to animportance-performance analysis. The value of this analysis is that it permits the identificationof improvement initiatives that can be subsequently addressed with targeted management activities.This is particularly relevant in light of today’s orientation on accountability and measurable results(see also Rust et al., 2004).

To conduct this analysis the following two steps were undertaken. First, to assess the performancelevel (i.e., y-axis), all indicators were rescaled to a 0 to 100 range. The resulting performance scores

1 We thank the editor and a reviewer for bringing this to our attention.




are summarized in the upper part of Figure 3. Second, the importance of each formative indicatoron strategic innovation capacity (i.e., x-axis) follows directly from the inner and outer model co-efficients presented in respectively Tables 1 and 3, and follows a similar procedure as the determi-nation of so-called total effects in a structural model. Note that the bootstrap results allow us toassess the statistical significance of the indicators’ importance in influencing strategic innovation ca-pacity. As evidenced by the figures presented in the upper part of Figure 3, all formative indicatorsthat have a statistically significant weight estimate (see also Table 1) also have a significant impacton strategic innovation capacity. Combining the importance and performance figures leads to theplot that is presented in the lower part of Figure 3.

The interpretation of the importance-performance analysis results is as follows. The importancescore indicates the change in strategic innovation capacity as a result of a one point increase in theindicator. The higher the importance score, the further to the right on the x-axis. As such, the morean indicator is located to the right in Figure 3 the higher its impact on strategic innovation capacity.For instance, if the score regarding “mechanisms that stimulate insight why non-customers aren’tcustomers” (i.e., item R08) increases by one point, strategic innovation capacity improves by 0.17.

The performance score provides an indication of the room for potential improvement. Thehigher the performance score, the less room for further improvement. This means for examplethat there is more room for improvement regarding the mechanisms tapped by item R08 than thosecaptured by item R03.

Disregarding investment costs of the various improvement initiatives for amoment, the results of theimportance-performance analysis suggest the following actions. The items in the relative right part of theplot (i.e., R03, R08, R06, A04, A05, and E01) indicate mechanisms that have a high impact on strategicinnovation capacity and are as such strategically important candidates for investments to at least main-tain the performance level of these elements (in this case, A04), or further improve the performance level(R03, R08, R06, A05, and E01). The items located on the relative left hand side of the plot still have a sta-tistically significant though lower impact on strategic innovation than the items located further on the

Figure 3. Results importance-performance analysis




right of the x-axis. For these lower-impact items as well, the relative position on the y-axis (i.e., perfor-mance) reflects their improvement potential. The practical implications stemming from theimportance-performance analyses are discussed in greater depth in the last section.

Discussion of the findingsThe study reveals an intricate pattern among on the one hand deliberate learning mechanisms andon the other hand strategic innovation capacity. To fully understand this phenomenon one has totake into account both the direct and indirect (i.e., mediated) effects of the variables. Taking intoaccount the entire nomological web put forward in our conceptual model (see also Figure 1) ourempirical results show that all three deliberate learning mechanism contribute positively and signif-icantly to the advance of an organization’s strategic innovation capacity. For the sake of clarity, webelow discuss the findings of our study per deliberate learning mechanism.

Deliberate learning mechanisms for recognitionHypothesis 1 could not be accepted, meaning that we could not find a positive direct effect of de-liberate learning mechanisms for recognition on strategic innovation capacity. However this doesby no means imply that this category of learning mechanisms is totally useless in stimulating stra-tegic innovation capacity. We found significant relations with learning mechanisms for assimilationand exploitation. The mediation analyses demonstrate indeed total mediation of the effects of rec-ognition mechanisms through learning mechanisms for assimilation and exploitation. Investing inmechanisms for recognition apparently only pays off on the condition exploitation, and assimila-tion in particular, are deliberately stimulated as well.

In other words, noticing environmental information will only lead to effective renewal if it firstleads to a renewed understanding (Becker, 2001). Marinova (2004) found that only when inter-pretation updates market knowledge, an increased innovation effort could be discerned; Hameland V€alikangas (2003) consequently refer to the term “cognitive challenge”. These results con-firm Zollo and Winter’s (2002) assertion about the value of deliberate learning mechanisms insituations of high exploration, such as strategic innovation. For instance, deliberate learningmechanisms for assimilation may be required to stimulate formal integration processes for orga-nizational information dissemination, interpretation and the identification of trends (Zahra andGeorge, 2002).

The formative measurement model that we applied enables us to pronounce upon the individualrecognition learning mechanisms as well. The performance-importance matrix (H€ock et al., 2010)shows the high importance of different recognition mechanisms. Of all learning mechanisms, mech-anisms stimulating a deep insight into current customers and into non-customers seem most im-portant. Overall, our results for the recognition mechanisms suggest a more modern, proactive,even “market-driving” form of market orientation (Narver et al., 2004; Tuominen et al., 2004;Yannopoulos et al., 2012). Non-local search (Nooteboom et al., 2007) and peripheral vision(Day and Schoemaker, 2004) might help to find new market opportunities and thus to facilitateentering new strategic domains (Danneels, 2003). For example, studying non-customers, other in-dustries, changes in the industry and future customer needs may prevent a “contraction of the op-portunity horizon” (Hamel and Prahalad, 1994). However, going too far away may becomecounter-productive, as shown by the irrelevance of studying end customer needs. Such informationmay produce new value propositions that deviate too much from the customer’s business percep-tion (Woodall, 2003).

The value of mechanisms that stimulate the consultation of innovative customers finally showsthe value of the lead user technique beyond the area of product development (Sharma et al., 2001).Lead users face new market needs earlier (L€uthje and Herstatt, 2004) and may help to uncover la-tent market needs (Amit and Schoemaker, 1993) and new customer solutions (Slater and Narver,1998; Yannopoulos et al., 2012). Our findings are hence in line with Lilien et al. (2002) who positthat a lead-user technique may systematically generate ideas for breakthrough innovations.




Deliberate learning mechanisms for assimilationHypothesis 2 was accepted; our analysis shows that learning mechanisms for assimilation even havethe largest effect size (see Table 3) on strategic innovation capacity.

In addition to their direct effects, deliberate mechanisms for assimilation are also mediated bymechanisms for exploitation. These mediation effects substantiate the argument of the ACAP liter-ature about the complementarity of the different ACAP dimensions (Lane and Lubatkin, 1998;Todorova and Durisin, 2007). However, the mediation effect is only partly. First, the remaining di-rect effect may provide evidence for Zahra and George’s (2002) argument that it is foremost the firsttwo dimensions of ACAP that enhance a firm’s strategic flexibility and its reconfiguration capacity.Other authors have also argued that in the context of strategic innovation, learning at a cognitivelevel is more fundamental than learning at the action level (Rajagopalan and Spreitzer, 1996). Asa consequence, the more extreme and path-breaking the level of innovation (such as SI), themore cognitive aspects (and thus assimilation, not exploitation) may come to the fore. A differentexplanation for the partial mediation effect may be that assimilation capacity is just more suscep-tible to deliberate stimulation than is exploitation. The sensemaking literature seems for examplemore concerned with steering cognitive aspects than behavioral aspects (e.g., Thomas et al.,2001). This argument would imply that although exploitation capacity may in and of itself stillhave an important mediating role for assimilation, this role may need less deliberate managerialfostering, or may be less susceptible to it. A third reason may be attributed to the operationalizationof strategic innovation capacity. Our focus on strategic innovation initiatives, standing somewhatmidway between full implementation (successfully commercialized) and initiation (concepts notyet materialized), could explain a lower need of exploitation as well.

The importance of the different learning mechanisms for assimilation (see weights andperformance-importance matrix) validates Slater and Narver’s (1995) proposition that exposingnew information to multiple interpretations and holding constructive discussions on long-heldassumptions about customers and markets lead to a redefinition of the business in a frame-breaking way. In this sense, the results also substantiate arguments developed in the literatureon strategic innovation regarding the importance of questioning the assumptions that a firmhas about its customers (Markides, 2004), markets and marketing approach in general (Hameland Getz, 2004; Slywotzky, 1996). The findings illustrate the value that stimulating collective crit-ical reflections and discussions may have for changing cognitive frameworks (Kuwada, 1998).Codifying (or filing) these reflections (Zollo and Winter, 2002) prove however not useful, mir-roring findings for product innovation that it is not a better storing or filing system but a bettersensemaking system in itself that directly affects a firm’s innovation capacity (De Luca andAtuahene-Gina, 2007; Dougherty et al., 2000). Overall, our findings on assimilation mechanismsseem to support the value that deliberate efforts may have for changing mental frameworks, asexemplified in the extant literature on sensemaking (Thomas et al., 2001) and provide furtherevidence to Barr et al.’s (1992) empirical findings that strategically proactive firms stay opento adjustments of their mental frameworks and show how constructive conflict can be nurturedwith the aim of entering new markets (Danneels, 2008) or uncovering new business models(Chesbrough, 2010).

Deliberate learning mechanisms for exploitationThe positive effect of deliberate learning mechanisms for exploitation leads to the acceptance ofH3. Stimulating specific elements in a company’s exploitation capacity helps fostering the com-pany’s strategic innovation capacity. A medium effect size was found. As already noted, deliber-ate mechanisms for exploitation also have an important mediating role for both the othercategories. These results seem to validate Thomas et al.’s (1993) proposition that noticing exter-nal information facilitates strategic action only through its effects on strategic interpretation. Inother words, deliberately increasing recognition capacity will affect strategic innovation capacityto a larger extent on the condition assimilation and exploitation capacity are triggered as well. Inthis respect, the findings empirically validate Zahra and George’s (2002) argument that for the




creation of ACAP as a coherent dynamic capability, all dimensions play complementary roles andbuild upon each other. Deliberately stimulating recognition, assimilation and exploitation capac-ity could then be considered as differential steps in the learning process (Lane and Lubatkin,1998).

The weights of the individual items, and the results of the performance-importance analysisgive an idea of the importance of the individual exploitation mechanisms. The effect of mecha-nisms that stimulate a change in organizational structure, ways of working, and even proceduresvalidate insights from the strategic innovation literature and illustrate how growth in new do-mains changes the established pattern of internal organization (Zander and Zander, 2005).Changes in these structural organizational aspects are perhaps positioned at a relatively funda-mental level and can be considered as a necessary condition to leverage the effects of mechanismstargeting other exploitation aspects (Christensen and Overdorf, 2000) Also, Doz and Kosonen(2010) stress the value of organizational aspects for the creation of strategic agility in the contextof new business models.

The insignificance of mechanisms that foster the replacement of organizational skills echoes dis-courses on strategic innovation whether competencies need replacing, developing, changing,stretching, reconfiguring or mere exploiting. Lilien et al. (2002) for instance found that break-through innovations seem to fit existing competencies surprisingly well as increased chances of ac-ceptance and funding will make them pass the organizational “screening” barrier more easily. Thisbrings further evidence to the argument that strategic innovators will be more inclined to leveragethan to completely transform their existing skills. Furthermore, strategic innovation initiatives,which are frame-breaking to the industry, are not necessarily frame-breaking to the initiating or-ganization (Baden-Fuller, 1995). The idea of combining, reconfiguring and leveraging e internaland external e resources in view of new value propositions has moreover been tackled in contri-butions to the resource-based view as well (Danneels, 2003; Lew and Sinkovics, 2012; Peteraf andBergen, 2003; Sirmon et al., 2007; Zander and Zander, 2005).

Conclusion and implicationsIn this paper we studied the deliberate learning mechanisms that firms can establish to foster theircapacity for strategic innovation. We re-conceptualized absorptive capacity from a cognitive per-spective and studied categories of deliberate mechanisms for recognition, assimilation and exploi-tation. Building on a literature review and a qualitative study we detected aspects in the threeabsorptive capacity dimensions that may foster a more “open-minded” recognition, assimilationand exploitation, and may in this respect seem pivotal for the stimulation of strategic innovationcapacity. We hypothesized positive associations between deliberate learning mechanisms that stim-ulate these aspects and a firm’s strategic innovation capacity, and performed a PLS analysis on dataobtained from Dutch industrial companies.

Theoretical contributionOur findings suggest the value of investing in absorptive capacity also for the sake of non-technological, path-breaking, strategic innovation. As such, our study contributes to research onabsorptive capacity, the dynamic resource-based view and strategic innovation.

First, we did one of the few attempts to stretch the absorptive capacity concept beyond a puretechnological context (Lane et al., 2006) and add a demand-oriented dimension (Murovec andProdan, 2009). Studying deliberate learning mechanisms we follow Cohen and Levinthal’s(1990) original (though barely answered) call to study organizational mechanisms for absorptivecapacity development. Results on the different learning mechanisms show that organizations caninfluence absorptive capacity even in domains where they lack prior experience (Lenox and King,2004). These findings question the strong path-dependent development of absorptive capacitythat several authors hypothesized (Bergh & Ngah-Kiing Lim, 2008; Cohen and Levinthal,




1990). Danneels’ (2003, 2008) empirical findings indeed show that experiential learning is ‘self-limiting’, constraining the range of market information and strategic options considered. Ourfindings on deliberate learning mechanisms reflect and illustrate the growing belief inthe value of deliberate learning efforts e in contrast to quasi-automatic experienceaccumulation e in the context of radically new strategic moves (Barkema and Schijven, 2008;Zollo and Singh, 2004) and business model rethinking (Sosna et al., 2010).

Our mediation analysis shows that deliberate mechanisms for assimilation and exploitation candirectly foster strategic innovation capacity. Yet, important mediation effects can be discerned aswell. In particular the effect of deliberate mechanisms for recognition seems fully mediated bythe two other categories of learning mechanisms. These findings provide early evidence for the ar-gument that the different dimensions of absorptive capacity build upon each other (e.g., Camisonand For�es, 2010; Lane and Lubatkin, 1998; Zahra and George, 2002): stimulating just one dimen-sion limits its full potential.

Many of the aspects that deliberate learning mechanisms foster in each of the absorptive capacitydimensions reflect the importance of sensemaking in absorptive capacity (Todorova and Durisin,2007; Van den Bosch et al., 2003; Volberda et al., 2010). In particular, the value of deliberate assim-ilation stimulation stresses the importance of shared mental models in an organizational informa-tion processing capability (Todorova and Durisin, 2007). Deliberate learning mechanisms forassimilation show the largest direct relationship with strategic innovation capacity. Firstly, thesefindings would corroborate the proposition that in the context of strategic innovation, learningat a cognitive level (essentially captured by assimilation capability) may be more fundamentalthan learning at the action level (Rajagopalan and Spreitzer, 1996). Secondly, assimilation may sim-ply be more susceptible to deliberate stimulation efforts than is exploitation. This argument wouldbe in line with the sensemaking literature that has emphasized the deliberate stimulation of cognitiveaspects more than the stimulation of behavioral aspects (Thomas et al., 1993). Infrequent, hetero-geneous and causally ambiguous events, such as strategic innovation initiatives, could require moredeliberate learning efforts just because they place higher cognitive demands (Zollo and Singh, 2004;Zollo and Winter, 2002).

Our study also contributes to the dynamic resource-based view (Teece et al., 1997). We followthe conceptualization of absorptive capacity as a dynamic capability (Narasimhan et al., 2006;Volberda et al., 2010) and distinguish specific aspects in the absorptive capacity-dimensionsthat are crucial to strategic innovation capacity. In this way, the abstract concept of dynamic ca-pabilities is made more tangible and action-oriented (Wang and Ahmed, 2007) and the specificmicro-foundations of capabilities are exemplified (Ethiraj et al., 2005; Ray et al., 2004). Our resultsdisclose valuable second-order learning mechanisms that firms may establish to develop dynamiccapabilities (Eisenhardt and Martin, 2000; Zollo and Winter, 2002) and illustrate the value of in-tentional managerial efforts in dynamic capability creation (Danneels, 2008). The interview datasuggest that the learning mechanisms stimulate specific goals, but do not specify in detail howthese goals should be reached. This implies that the stimulated processes for recognition, assimi-lation and exploitation stay open to constant change (D’Adderio, 2008; Pentland and Rueter,1994). For instance, even though cross-functional discussions about the market may take theform of fixed appointments, their formula often changes (e.g., by inviting external people). Themechanisms’ semi-structured form provides evidence for Danneels’s (2002) argument thatsecond-order competences may mitigate the effects of path dependencies, a prerequisite for en-abling processes of “experimentation and effectuation and leadership” for change in businessmodels (Chesbrough, 2010).

Finally, our findings attempt to enhance both the theoretical development and the managerialrelevance of growing research on strategic and business model innovation (Baden-Fuller andMorgan, 2010; Markides, 2006). The aspects that deliberate learning mechanisms foster add to in-sights on strategic innovation creation (Leifer et al., 2001). Our findings on exploitation corrobo-rate the theoretical distinction between the internal and external facets of frame-breakinginnovation (Baden-Fuller, 1995) and mirror the viewpoint that for competitive advantage, it is




not rareness of resources in terms of resource type that matters, but rareness in resource function-ality (Peteraf and Bergen, 2003; Zander and Zander, 2005).

Managerial relevanceEven though it takes time to develop deliberate learning mechanisms (Barkema and Schijven, 2008),they are expected to provide managers with handles to stimulate strategic innovation capacity ona still shorter term than can be done by the creation of generally cited enablers such as an innovativeorganizational culture (e.g., Danneels, 2008; Markides, 1999). This is particularly important giventhat strategic innovation can be very remunerative in terms of growth and profits (Kim andMauborgne, 1999; Larsen et al., 2002).

Our results suggest that if firms want to gain full effects of these mechanisms, it might be impor-tant to establish all three categories of learning mechanisms. The considerable and direct effect ofdeliberate learning mechanisms for assimilation shows that firms should critically reflect on theirprevalent assumptions about the market, customers and the marketing approach. We hope thisfinding might also inspire firms in their knowledge management designs, and will restrain themfrom becoming “information-rich, but interpretation-poor systems” (Prahalad and Bettis,1995: 6). Tsai and Shih (2004) for example demonstrated that firms can enhance their marketingcapabilities by the application of “marketing knowledge management” and Day (2002) has pleadedfor “maps of the market-learning processes” in the organization. These maps describe in detail themarket information flow in the organization: where does the information enter the organization,how is it distributed and how can it be retrieved?

In addition to the overall effects of the three categories of deliberate learning mechanisms theformative specification of the learning mechanisms also makes a very concrete translation intomanagerial interventions possible. Moreover, the results of the importance-performance matrix(see Figure 3) provide valuable information on the relative impact of the specific learning mech-anisms on strategic innovation, and consequently on business returns as well (Calantone et al.,2002). This implication is essential as all functional disciplines within a firm are held increasinglyfinancially accountable for their strategic decisions (Seggie et al., 2007). In response to this trendStreukens et al. (2011) proposed a mathematical framework allowing managers to explicitlytradeoff the revenues and costs associated with strategic decisions in order to make optimal(marketing) investment decisions. In terms of the Streukens et al. (2011) Return on Marketingmodel this study’s empirical results constitute essential building blocks for the model’s revenuefunction.

Disregarding investment costs for a moment, as data are not available and investment costs forthe different mechanisms are company- and industry-specific, the several concrete marketing in-vestment implications stemming from our empirical results can be presented in Figure 4. To setactionable priorities we took the median performance (i.e., 50) and importance (i.e., 0.10) scores(see Figure 3) as arbitrary cut-offs to enable a good comparison among the different mechanisms,leading to four categories (i.e., quadrants). Assuming equal investment costs, mechanisms in thetop and bottom right quadrants of Figure 4 provide the highest pay-offs. Especially the lower-right quadrant contains priority levers that highly affect strategic innovation capacity and thatalso leave much room much for improvement. The upper-right-hand quadrant contains effectivemechanisms but with a smaller potential improvement. There, firms are suggested to take foremostvery good care of their current mechanisms, and to reinforce their existing advantage. The left-handquadrants are of lesser importance, although it should be noted that all mechanisms there do stillproduce significant improvements in strategic innovation capacity as well.

The results overall imply that firms should emphasize developing a more modern, proactive mar-ket orientation for exploration ends (e.g., Yannopoulos et al., 2012). Firms need to think aboutchanges in their broader business environment and may extend their market research towards otherindustries and non-customer needs. New value propositions can be discovered by linking this in-formation to the firm’s competencies. Close relationships with existing, and in particular innovativecustomers, seem to pay off as well (Tuominen et al., 2004). These findings may point to a new,




Figure 4. Managerial implications

extended role for account managers and all “people in the field” (such as service engineers) as “mar-ket and idea antennas”. In addition, it seems highly valuable to frequently and critically discuss andshare assumptions about all aspects of the marketing approach (e.g., distribution). Finally, the im-portance of changing organizational structures suggests how the learning mechanisms should beanchored within the entire organization. These results may therefore not only question the valueof separate business development departments for strategic innovation, they furthermore evidenceHarris’ (2000) paradox that developing and external orientation (i.e., new and superior customervalue) chiefly rests on internal, organizational characteristics.

Limitations and directions for future researchFirstly, our findings do not enable us to pronounce any normative judgments on the appropri-ateness of strategic innovation as the strategic route to pursue. The question of being better orbeing different than competitors is foremost a cost-benefit trade-off (Winter, 2003; Zott,2003) and depends on the organizations’ core competences and on the industry’s evolutionand dynamism (Floyd and Lane, 2000; Markides, 2000). Future research on the economic andfinancial consequences of strategic innovation (Mizik and Jacobson, 2003; Prietula andWatson, 2000) is hence required. Also, we focus on self-reported portfolios of strategic innova-tion initiatives which do not necessarily guarantee success in commercializing the portfolio ofstrategic innovation initiatives on the market. The integration of more detailed (financial) outputvariables could shed light on the interaction of experience accumulation and deliberate learning(Kale and Singh, 2007).

Secondly, the influence of additional moderators needs further studying. As with every studythere is always a trade-off between internal and external validity. Given the variety of firm sizesand industries in our sample unobserved heterogeneity is a critical issue to address in future re-search. A FIMIX-PLS analysis could be run to formally address whether unobserved characteristicsmay have an impact on the structural model parameter estimates (Ringle et al., 2010) and thus,whether significant moderator effects exist. Of course, both internal factors (such as the existenceof an innovative culture (e.g., Hurley and Hult, 1998)) and external factors (such as the degree ofcollaboration with customers (e.g., Slater and Narver, 2000)) might impact on the effectiveness ofthe learning mechanisms. As such, we invite fellow researcher to identify potential moderators infuture research.

Finally, the cross-sectional character of our study did not reveal detailed process insights.Although not the focus of this study, qualitative results and statistical analyses (e.g., mediationtests) suggested important interrelationships among the three categories of learning mechanisms.




In-depth longitudinal process research on the creation of strategic innovation initiatives is re-quired to study the existence of real causality among the learning mechanisms, or among the un-derlying absorptive capacity dimensions. Such studies could shed light on the (non-)linearity ofthe absorptive capacity process (Lane et al., 2006, Todorova and Durisin, 2007), and on the ex-istence of potential positive or negative feedback effects caused by expectation formation (Vanden Bosch et al., 2003), broader assimilation structures (Hargadon and Fanelli, 2002) or over-search effects (Ahuja and Lampert, 2001). In contrast to our focus on “flow” variables (learningmechanisms), the inclusion of additional effects of “stocks” of existing recognition, assimilation,exploitation capabilities could reveal the precise direction of causality. Such additional analysescould provide more validated explanations of mediation effects.

Appendix

Measures of all variables

Control variables

� Position in supply chain: Indicated on a 10-point scale with 1: raw materials supplier, and 10,end customer

� Business unit (firm) type: Type of product or services mainly offered. Product firms are cate-gorized into 7 different product categories (e.g., machine components, semi-manufactures)and service firms into 6 service categories (e.g., wholesale, dealership).

� Business unit (firm) size: Number of FTEs in business unit (firm)

Deliberate learning mechanisms for recognition

R01 We use mechanisms that stimulate us to focus our market research more on future customer needs than

on current customer needs.

R02 We use mechanisms that stimulate us to detect fundamental changes in our industry (e.g., technology,

competitors, regulation).

R03 � We use mechanisms that stimulate us to study the likely effect that changes in our business environment

will have on our customers.

� We use mechanisms that stimulate us to study the behavior of our customers throughout the different

experience stages of our product/service (e.g., search, purchase, use, disposal).

� We use mechanisms that stimulate us to reveal trends in the behavior of our customers throughout the

different experience stages of our product/service.

� We use mechanisms that stimulate us to study how the different features of our products/services meet

the needs of our customers throughout the different experience stages of our product/service.

R04 � We use mechanisms that stimulate us to frequently collect and evaluate general macro-economic in-

formation (e.g., interest rate, exchange rate, GDP, industry growth rate, inflation).

� We use mechanisms that stimulate us to maintain contacts with officials of government regulatory

bodies (e.g., Ministry of Economic affairs, Chambers of Commerce) in order to collect and evaluate

pertinent information.

� We use mechanisms that stimulate us to collect and evaluate information concerning general societal

trends (e.g., environmental consciousness) that might affect our business.

R05 We use mechanisms that stimulate us to consult innovative customers for new, interesting business ideas.

R06 We use mechanisms that stimulate us to focus our market research on other industries as well.

R07 We use mechanisms that stimulate us to retrieve information about the needs of the end customer.

R08 We use mechanisms that stimulate us to gain a well-founded insight into the reasons why our non-

customers aren’t our customers.




Deliberate learning mechanisms for assimilation

A01 We do not use any mechanisms that stimulate us to share our critical reflections about our customers/

market with each other (reversely coded).

A02 We use mechanisms that stimulate us to frequently discuss the assumptions that we have about our

customers.

A03 We use mechanisms that stimulate us to frequently discuss the assumptions that we have about our

market(s).

A04 We use mechanisms that stimulate us to keep alive past critical reflections about our customers/market.

A05 We use mechanisms that stimulate us to frequently discuss the assumptions that we have about all aspects

of our market(ing) approach.

A06 We use mechanisms that stimulate us to systematically file our critical reflections about customers/market

(e.g., in a data base, on the intranet).

Deliberate learning mechanisms for exploitation

E01 We use mechanisms that stimulate us to adapt the organizational structure to better cater the needs of

a (planned) new offering.

E02 We use mechanisms that stimulate us to replace our skills (competencies) to better cater the needs of


E03 We do not use any mechanisms that stimulate us to prevent chaos in view of a (planned) new offering

(reversely coded).

E04 We use mechanisms that stimulate us to support new projects, even if they possibly may take away from

sales of existing products/services.

E05 We do not use any mechanisms that stimulate us to adapt our procedures to better cater the needs of

a (planned) new offering (reversely coded)

E06 We use mechanisms that stimulate us to change our ways of working to better cater the needs of


Strategic innovation capacity
In order to create new and substantially superior customer value, we take, in comparison to our competitors:
S01 .more initiatives to collaborate in an untraditional way (i.e., unusual in our industry) with parties in our

supply chain, such as suppliers or customers.

S02 . more initiatives to collaborate in an untraditional way (i.e., unusual in our industry) with parties

outside our supply chain.

S03 . more initiatives to change the traditional roles and relationships in our industry.

S04 . more initiatives to change our business model.

S05 . more initiatives to create a market approach that is unusual in our industry.

S06 . more initiatives to break the traditional power relationships among the different parties in the supply

chain.

S07 . fewer initiatives to deviate from the traditional rules of the game (reversely coded).

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BiographiesLiselore Berghman is Assistant Professor of Strategy at the VU University Amsterdam and guest professor at the

University of Antwerp. Her research focuses on strategic marketing and strategic innovation in industrial com-

panies, and on market & customer orientation in service companies. She has published in journals such as In-

dustrial Marketing Management, The Service Industries Journal, British Food Journal, International Journal of

Innovation Management and the Journal of Managerial Psychology. Before joining the VU she worked as a strategy

consultant (The Boston Consulting Group). E-mail: [email protected]

Paul Matthyssens is Professor of Strategic Management at the Department of Management (Faculty of Applied Eco-

nomics, University of Antwerp, Belgium) and the Antwerp Management School (Belgium). His research focuses on

market strategy in international and business-to-business markets and purchasing & value creation. His work has

been published in journals such as Long Range Planning, Journal of Management Studies, Corporate Governance: An

International Review, Strategic Organization, Industrial Marketing Management, Psychology & Marketing, Journal of

Business & Industrial Marketing, Journal of International Marketing, International Marketing Review, Journal of

International Management, Technovation and the Journal of Purchasing & Supply Management.

E-mail: [email protected]

Sandra Streukens is an Assistant Professor at the Department of Marketing and Strategy at Hasselt University (Bel-

gium). Her research interests include services marketing, value-based marketing, and marketing research meth-

odology. Her work has been published in journals such as Industrial Marketing Management, Marketing Letters,

Journal of Economic Psychology, Information & Management, and Journal of Service Research.


Koen Vandenbempt is Professor of Strategic Management at the Department of Management (Faculty of Applied

Economics, University of Antwerp, Belgium) and the Antwerp Management School (Belgium). His research focuses

on the process of strategic reorientation efforts within industrial companies. His work has been published in

journals such as Human Relations, Technovation, Industrial Marketing Management, Journal of Business & Industrial

Marketing, Advances in Business Marketing & Purchasing, and Advances in Applied Business Strategy.





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mailto:[email protected]




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