Direct and configurational paths of absorptive capacity and organizational innovation to successful organizational performance

JBR-09113; No of Pages 7 Journal of Business Research xxx (2016) xxx–xxx

Contents lists available at ScienceDirect

Journal of Business Research

Direct and configurational paths of absorptive capacity and organizational innovation to successful organizational performance☆ Murad Ali a,⁎, Konan Anderson Seny Kan b, Marko Sarstedt c,d a

Department of HRM, Faculty of Economics and Administration, King Abdulaziz University, P.O Box 80201, Jeddah 21589, Saudi Arabia University of Toulouse, Toulouse Business School, 20, bd Lascrosses - BP 7010 - 31068 Toulouse Cedex 7, Toulouse, France Otto-von-Guericke-University Magdeburg, Universitaetsplatz 2, 39106 Magdeburg, Germany d University of Newcastle, Faculty of Business and Law, Australia b c

a r t i c l e

i n f o

Article history: Received 1 January 2016 Received in revised form 1 March 2016 Accepted 1 April 2016 Available online xxxx Keywords: Absorptive capacity Innovation Performance PLS-SEM fsQCA Predictive analysis

a b s t r a c t This study investigates how firms can achieve high levels of organizational performance under different configurations of absorptive capacity and organizational innovation. The study uses partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to test relationships among dimensions of absorptive capacity, organizational innovation, and organizational performance. The results provide support for the absorptive capacity's role for organizational innovation and performance. Furthermore, different configurations of absorptive capacity and organizational innovation conditions lead to better organizational performance. © 2016 Elsevier Inc. All rights reserved.

1. Introduction Research shows that absorptive capacity (ACAP), which is a firm's “ability to recognize the value of new information, assimilate it, and apply it to commercial ends” (Cohen & Levinthal, 1990, p. 128), plays a fundamental role in the development of firms' innovative capabilities and performance (e.g. Camisón & Villar-López, 2014, Cepeda-Carrion, Cegarra-Navarro, & Jimenez-Jimenez, 2012, Chen, Lin, & Chang, 2009). ACAP is a multidimensional concept that comprises acquisition, assimilation, transformation, and exploitation of knowledge (Zahra & George, 2002). While prior research acknowledges the importance of ACAP for firms (Camisón & Forés, 2010), to date, no study analyzes empirically

☆ This article was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah. The authors, therefore acknowledge with thanks DSR for technical and financial support. The authors thank the anonymous reviewers, the JBR editors, and the participants at the 2016 GIKA conference held in Valencia, Spain, for their constructive comments on the study. The authors also thank Mohammad Asif Salam and Imran Ali, King Abdulaziz University, Saudi Arabia for their careful reading and suggestions, as well as Jin Ji Piao, Cheonsik Park and Eungyeong Bahk for their support on data collection. This study receives the Best Paper Award at GIKA 2016 held in Valencia, Spain. ⁎ Corresponding author. E-mail addresses: [email protected] (M. Ali), [email protected] (K.A. Seny Kan), [email protected] (M. Sarstedt).

each individual dimension's role in explaining innovative capabilities and performance. More precisely, prior research on the effects of ACAP draws on a unidimensional (Forés & Camisón, 2015) or twodimensional (Ali & Park, 2016; Leal-Rodríguez, Ariza-Montes, Roldán, & Leal-Millán, 2014) conceptualization of the ACAP construct, instead of clearly differentiating between dimensions. However, acquisition, assimilation, transformation, and exploitation are fundamentally distinct concepts that involve different objectives, structures, and strategies (Cepeda-Carrion et al., 2012). Against this background, this study sheds light on whether these four dimensions provide the same (or different) results for a firm when considering them separately (Jansen, Van Den Bosch, & Volberda, 2005). More precisely, this study examines the four ACAP dimensions' effect on the three dimensions of organizational innovation (i.e., product, process, and management-related innovation), and, finally, on organizational performance. By simultaneously considering antecedents and contingencies of ACAP and organizational innovation, this study offers a holistic model that captures the complexity of the relationship among the variables reflecting ACAP and organizational innovation processes involved in organizational performance. Finally, by using the fuzzy-set qualitative comparative analysis (fsQCA) (Fiss, 2011; Cheng, Chang, & Li, 2013; Ganter & Hecker, 2014; Woodside, 2016), this study identifies distinct mechanisms through which ACAP and organizational innovation set to achieve higher levels

http://dx.doi.org/10.1016/j.jbusres.2016.04.131 0148-2963/© 2016 Elsevier Inc. All rights reserved.

Please cite this article as: Ali, M., et al., Direct and configurational paths of absorptive capacity and organizational innovation to successful organizational performance, Journal of Business Research (2016), http://dx.doi.org/10.1016/j.jbusres.2016.04.131

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of organizational performance. As such, this study answers the call for a more nuanced exploration of the complex causal relationships between antecedents and contingencies of organizational performance (Ren, Tsai, & Eisingerich, 2015).

that possess ACAP are likely to have a better understanding of new technology, which can generate new ideas and develop new product, process and management innovation (Tsai, 2001). Therefore:

2. Theoretical background and hypotheses

H1. Acquisition relates positively to product innovation, process innovation, and management innovation.

2.1. Absorptive capacity and organizational innovation

H2. Assimilation relates positively to product innovation, process innovation, and management innovation.

Zahra and George (2002) distinguish four dimensions of ACAP: acquisition, assimilation, transformation, and exploitation. Furthermore, these authors distinguish between potential absorptive capacity (PACAP), which comprises the first two dimensions, and realized absorptive capacity (RAPAC), which comprises the latter two dimensions. Acquisition refers to a firm's capability to initiate, identify, value, acquire, and gather relevant knowledge that is critical to its operations from external sources (Zahra & George, 2002). Assimilation refers to a firm's capability to assimilate or absorb externally generated knowledge, enabling the firm to analyze, process, and interpret this externally acquired knowledge through its own specific processes and routines. This assimilation helps the firm to understand, internalize, and further classify the knowledge (Zahra & George, 2002). Transformation describes the degree to which a firm develops and refines those internal routines, which facilitates combining existing knowledge with the newly acquired and assimilated knowledge for future use (Zahra & George, 2002). Finally, exploitation measures a firm's capability to use and implement the acquired, assimilated, and transformed knowledge, along with its existing routines, operations, competences, and technologies. This process not only improves the firm's existing operations, routines, and competences but also creates new organizational ones, including new product innovation, process innovation, and management innovation (Jiménez-Barrionuevo, García-Morales, & Molina, 2011; Zahra & George, 2002). In terms of effects of ACAP, this study distinguishes three innovation categories: product, process, and management innovation. Product innovation refers to the introduction of a good or service that is new or significantly improved with respect to its characteristics or intended uses (Damanpour, 1991, 1996). Process innovation is the introduction of new elements into the firm's production or service process, to produce better product or provide better service (Damanpour, 1991, 1996). Management innovation consists on the implementation of a new organizational method in the firm's business practices, workplace organization, or external relations. Previous research on ACAP-innovation interaction shows that the ACAP has a significant impact on organizational innovation (Chen et al., 2009; Jiménez-Jiménez & Sanz-Valle, 2011; Leal-Rodríguez et al., 2014; Tsai, 2001). ACAP increases a firm's ability to apply new knowledge to produce more innovations and improve the business operations and performance. Thus, the ability of a firm to recognize the value of new external information, assimilate, and apply new external knowledge to commercial ends is a critical factor to firms' innovative capabilities (Cohen & Levinthal, 1990). Zahra and George (2002) also argue that ACAP is a primary source of innovation and performance improvements. Research shows that absorptive capacities affect the effectiveness of innovation activities (Chen et al., 2009). ACAP is one of the most important determinants of the firm's ability to acquire, assimilate, and effectively utilize new knowledge to increase innovation. Firms with well-developed ACAP are more likely to pursue product, process, and management innovation. Firms with a strong ACAP are capable to acquire new external knowledge, combine the acquired knowledge with their prior related knowledge, and transform and exploit the new knowledge in the product, process, and management innovation (Leal-Rodríguez et al., 2014). Consequently, firms make efforts to increase absorptive capacities to acquire, assimilate, transform, and exploit new and external knowledge, which contributes to achieve high performance in product, process, and management innovation. Firms

H3. Transformation relates positively to product innovation, process innovation, and management innovation. H4. Exploitation relates positively to product innovation, process innovation, and management innovation.

2.2. Organizational innovation and performance Numerous studies evidence a positive relationship between organizational innovation and firm performance. For example, Camisón and Villar-López (2014) show how product, process, and management innovation separately affect firm performance. Similarly, JiménezJiménez and Sanz-Valle (2011) show that product, process, and administrative innovation jointly influence organizational performance positively. Therefore, this study proposes the following final set of hypotheses. H5. Product innovation, process innovation, and management innovation relate positively to organizational performance. The hypothesized relationships suggest a causal chain leading from ACAP (acquisition, assimilation, transformation, and exploitation) and organizational innovation (product, process, and management) to organizational performance. Jansen et al. (2005) use four dimensions of ACAP separately instead of PACAP, RACAP, or ACAP. All the four dimensions of ACAP coexist and participate in the improvement of organizational innovation and performance. Research studies also show that the relationship between innovation and performance is complex and requires more research (Jiménez-Jiménez & Sanz-Valle, 2011). Camisón and Villar-López (2014) show that the interrelation of product, process, and management innovation provides a better understanding of how firms benefit from these types of innovation to obtain superior firm performance. The representation of organizational innovativeness is more accurate when considering multiple rather than single innovation. Firm performance may depend more on the congruency between innovations of different types than on each type alone (Damanpour, 1991). Damanpour (1991) suggests that to enhance performance, firms invest in product and intraorganizational process innovations synchronously, rather than in product innovations alone. This suggests the existence of complex configurations of ACAP and organizational innovation dimensions associated with organizational performance. In line with this perspective, this study posits the following hypothesis: H6. Varied combinations of ACAP (acquisition, assimilation, transformation, and exploitation) and organizational innovation (product, process, and management) associate with superior organizational performance. This study tests this last hypothesis with fsQCA, which suits the aim of gaining a deeper understanding of the interconnected structures of the constructs and the complex nature of their interdependencies. Recently, management scholars suggest that the analysis of configurations plays a crucial role in organization research (Fiss, 2011; Seny Kan, Adegbite, El Omari, & Abdellatif, 2015). The identification and analysis of causal conditions' configurations that improve organizational performance provide a more detailed picture and allow for rich insights. These analyses contribute to understand complex causal relationships and the effect of causal recipes of improved organizational performance. Such

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insights may help managers prioritize necessary resources and capabilities and improve the efficiency and effectiveness of organizational performance. 3. Method 3.1. Data collection and sample This study draws on Ali and Park's (2016) data set, which focuses on large, medium, and small sized industrial firms in South Korea. Recently, Korean firms are increasingly recognizing the advantages that the new knowledge management initiatives can bring in terms of innovation excellence. The sample includes companies from various sectors and not exclusively knowledge-intensive ones, because any organization has the potential to achieve a high level of knowledge performance (PertusaOrtega, Molina-Azorín, & Claver-Cortés, 2010). This study ensured that all the sample firms maintained similar applications and organization resources, alleviating the moderating effects of the economy and industry. The fieldwork yielded a total of 347 complete and valid responses. The study used an average scores approach for firms with multiple respondents. The final sample for statistical analyses consisted of 195 responses.

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particularly suitable for analyzing complex path models, which covariance-based SEM techniques typically cannot estimate (Ringle, Sarstedt, & Straub, 2012). Furthermore, this study focuses on predicting organizational innovation and performance by means of the ACAP dimensions (Hair, Hult, Ringle, & Sarstedt, 2014), which calls for the use of PLS-SEM as a prediction-oriented approach to SEM (Rigdon, 2012). This study employs fsQCA (Ragin, 2008) to address H6. The fsQCA pioneers posit the use of QCA analysis in a regression-based framework (Woodside, 2016). Therefore, in this study, fsQCA analysis complements the PLS-SEM results by identifying configurations of ACAP and organizational innovation for achieving high levels of organizational performance (Schlittgen, Ringle, Sarstedt, & Becker, 2016). Seny Kan et al. (2015) argue that fsQCA is a novel way to access knowledge on organizations and management issues. 4. Results To analyze the PLS path model, this study uses the SmartPLS 3 software (Ringle, Wende, & Becker, 2015). Following Hair et al. (2014), the results' interpretation comprises two stages: (1) assessment of the measurement model, and (2) evaluation of the structural model.

3.2. Measures 4.1. Measurement model The questionnaire builds on the literature review from Section 2. This study adapts tested and proven measures from prior studies where the items and responses appear on a five-point Likert scale, ranging from “1: strongly disagree” to “5: strongly agree”. To assess the four dimensions of ACAP, this study adapts the scale (three items to measure acquisition, four items to measure assimilation, four items to measure transformation, and three items to measure exploitation) from Flatten, Engelen, Zahra, and Brettel (2011). This study takes measures for product innovation (six items), process innovation (five items), and management innovation (seven items) from Liao, Fei, and Chen (2007). The four-item scale for performance is from Reinartz, Krafft, and Hoyer (2004). 3.3. Data analysis To analyze the research model in Fig. 1 this study employs partial least squares structural equation modeling (PLS-SEM). PLS-SEM is

The results show that the measurement models meet all minimum requirements, as Table 1 shows. First, most indicator loadings are above 0.70, supporting the indicators' reliability. Only three indicators show lower loadings but as the corresponding constructs present satisfactory levels of internal consistency reliability and convergent validity, the analysis follows Hair et al. (2014) and retains the indicators. Second, all composite reliabilities and Cronbach's alpha are greater than 0.70, thus confirming the measures' internal consistency reliability. In addition, all average variance extracted (AVE) values surpass the threshold of 0.50, supporting the construct measures' convergent validity. Finally, the analysis confirms discriminant validity by (1) comparing the square root of AVE to the correlations (Table 2), (2) a cross loading analysis, and, most importantly, because (3) the values of the heterotrait-monotrait ratio of correlations (HTMT) (Henseler, Ringle, & Sarstedt, 2015) are below the (conservative) threshold of 0.85 (Table 2).

Fig. 1. The conceptual model.

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Table 1 Measurement model. Construct

Items

SL

C.R

α

AVE

Acquisition

AC1 AC2 AC3 AS1 AS2 AS3 AS4 TR1 TR2 TR3 TR4 EX1 EX2 EX3 PD1 PD2 PD3 PD4 PD5 PD6 PR1 PR2 PR3 PR4 PR5 MG1 MG2 MG3 MG4 MG5 MG6 MG7 P1 P2 P3 P4

0.85 0.86 0.82 0.80 0.75 0.77 0.84 0.69 0.80 0.82 0.82 0.84 0.92 0.86 0.80 0.64 0.72 0.86 0.86 0.82 0.87 0.85 0.81 0.79 0.53 0.58 0.72 0.78 0.77 0.73 0.81 0.81 0.83 0.86 0.87 0.83

0.88

0.80

0.71

0.87

0.80

0.63

0.86

0.79

0.62

0.91

0.85

0.76

0.91

0.83

0.62

0.88

0.83

0.61

Assimilation

Transformation

Exploitation

Product innovation

Process innovation

Management innovation

Performance

power (Sarstedt, Ringle, Henseler, & Hair, 2014). Similarly, results from blindfolding with an omission distance of 7 yield Q2 values well above zero (Table 3), thus supporting the model's predictive relevance in terms of out-of-sample prediction (Hair, Sarstedt, Ringle, & Mena, 2012). Further analysis of the composite-based standardized root mean square residual (SRMR) yields a value of 0.059, which confirms the overall fit of PLS path model (Hair et al., 2014; Henseler et al., 2014). Applying the bootstrapping procedure (5000 bootstrap samples; no sign changes) provides the p-values as well as the corresponding 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals (Table 3). The empirical results support the vast majority of hypothesized path model relationships among the constructs. Most notably, ACAP's acquisition, assimilation, and exploitation dimensions positively and significantly influence organizational innovation. More precisely, while the exploitation dimension strongly depends on product and process innovation, the assimilation dimension strongly relies on management innovation. However, contrary to the hypotheses, transformation plays no role in driving organizational innovation, as this ACAP dimension does not affect either of the innovation dimensions. Finally, process and management innovation, but not product innovation, positively and significantly influence organizational performance. 4.3. Predictive validity of PLS path model

0.90

0.87

0.56

0.91

0.87

0.72

Note: SL = standardized loading; C.R = composite reliability; α = Cronbach's alpha; and AVE = average variance extracted.

Following the procedure in Cepeda-Carrión, Henseler, Ringle, & Roldán (2016), this study reports the analysis of the predictive validity (Woodside, 2016) of the PLS path model as follows: first, from the total sample, the study selects 137 samples as the training sample and the remaining 58 samples as the holdout sample. Second, the study normalizes each observation in the holdout sample. Third, after performing estimation with the training sample, the study normalizes the construct scores. Four, for the organizational performance in the holdout sample, the path coefficients resulting from the training sample contribute to the creation of the predictive scores. Finally, for organizational performance, the correlation between the predictive scores and actual scores is r = 0.64 (p b 0.001) suggesting that the original model has acceptable predictive validity.

4.2. Structural model 4.4. fsQCA approach The analysis of the structural model's results draws on Hair et al. (2014). The analysis shows minimum collinearity in each set of predictors in the structural model, as all the variance inflation factor (VIF) values are far below the threshold of 5 (Hair, Ringle, & Sarstedt, 2011). Furthermore, the R2 values of product innovation (0.31), process innovation (0.53), management innovation (0.39), and performance (0.29) are in line with prior research (e.g. Camisón & Villar-López, 2014, Cepeda-Carrion et al., 2012, Chen et al., 2009), supporting the model's in-sample predictive

fsQCA uses Boolean algebra to generate combinations of causal conditions leading to an outcome. Central to the fsQCA approach are the calibration procedure and the truth table analysis. The calibration is a transformation process consisting in converting conventional measures into fuzzy sets. The truth-table analysis produces three different solution terms: (1) complex, (2) parsimonious, and (3) intermediate (Rihoux & Ragin,

Table 2 Means, standard deviations, correlations, and discriminant validity results. Mean

S.D.

1

2

3

4

5

6

7

8

ACAP 1. Acquisition 2. Assimilation 3. Transformation 4. Exploitation

3.63 3.61 3.64 3.34

0.80 0.81 0.66 0.82

0.84 0.39⁎⁎ 0.36⁎⁎ 0.37⁎⁎

0.48 0.79 0.51⁎⁎ 0.52⁎⁎

0.46 0.62 0.79 0.51⁎⁎

0.44 0.63 0.61 0.87

0.43 0.45 0.57 0.39

0.57 0.67 0.76 0.58

0.45 0.65 0.58 0.50

0.34 0.49 0.41 0.39

Organizational innovation 5. Product innovation 6. Process innovation 7. Management innovation

3.20 3.39 3.31

0.81 0.72 0.70

0.37⁎⁎ 0.47⁎⁎ 0.38⁎⁎

0.41⁎⁎ 0.56⁎⁎ 0.56⁎⁎

0.35⁎⁎ 0.49⁎⁎ 0.43⁎⁎

0.51⁎⁎ 0.65⁎⁎ 0.49⁎⁎

0.79 0.62⁎⁎ 0.48⁎⁎

0.73 0.78 0.61⁎⁎

0.55 0.71 0.75

0.39 0.57 0.52

Organizational performance 8. Performance

3.39

0.80

0.29⁎⁎

0.42⁎⁎

0.33⁎⁎

0.36⁎⁎

0.37⁎⁎

0.48⁎⁎

0.48⁎⁎

0.85

Note: ⁎⁎Correlation is significant at the 0.01 level (2-tailed). Diagonal and italicized elements are the square roots of the AVE (average variance extracted). Below the diagonal elements are the correlations between the constructs values. Above the diagonal elements are the HTMT values.

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Table 3 Significant testing results of the structural model path coefficients. Structural path

Path coefficient

t-value

p-value

95% BCa confidence interval

Conclusion

Acquisition → Product Acquisition → Process Acquisition → Management Assimilation → Product Assimilation → Process Assimilation → Management Transformation → Product Transformation → Process Transformation → Management Exploitation → Product Exploitation → Product Exploitation → Management Product → Performance Process → Performance Management → Performance SRMR composite model = 0.06 R2Product = 0.31; Q2Product = 0.18 R2Process = 0.53; Q2Process = 0.32 R2Management = 0.39; Q2Management = 0.21 R2Performance = 0.29; Q2Performance = 0.20

0.17 0.20 0.13 0.14 0.22 0.35 0.04 0.10 0.09 0.35 0.41 0.22 0.06 0.27 0.29

2.13 3.15 1.85 1.73 2.99 3.95 0.43 1.45 1.31 4.30 6.14 2.61 0.60 2.86 3.80

0.03 0.00 0.07 0.08 0.00 0.00 0.67 0.15 0.19 0.00 0.00 0.01 0.56 0.00 0.00

(0.02, 0.33) (0.09, 0.32) (0.01, 0.23) (0.00, 0.31) (0.06, 0.36) (019, 0.53) (−0.14, 0.21) (−0.03, 0.25) (−0.03, 0.25) (0.20, 0.50) (0.28, 0.54) (0.06, 0.37) (−0.12, 0.27) (0.08, 0.45) (0.16, 0.44)

H1 supported

2009). Ragin and Fiss (2008) and Fiss (2011) propose the mix of the last two solutions to bring out core and peripheral conditions, associated with the outcome of interest. Core conditions are solutions belonging to both parsimonious and intermediate that show a strong causal relationship with the outcome, whereas peripheral conditions are solutions appearing only in the intermediate solutions and presenting a weaker relationship with the outcome. 4.4.1. Calibration procedure In this study, the transformation of the Likert scales to fuzzy sets is possible for each latent variable by calculating average values of their items. Next, the study calibrates conditions and outcome variables into fuzzy sets using the direct calibration method (Ragin, 2008). Afterwards, this study specifies three qualitative anchors for the calibration: (1) full membership to respondents having a membership score greater than or equal to 0.95; (2) full non-membership for a threshold of 0.05, and (3) 0.50 for the cross-over point, which indicates a point where respondents are neither in nor out of the set of interest. Following related research (Fiss, 2011), this study applies the 75th percentile of each variable as an anchor to full membership. Consequently, the study uses the 25th percentile for the full non-membership and the 50th percentile for the cross-over point.

H2 supported

H3 not supported

H4 supported

H5 is partially supported

transforming and exploiting such knowledge. Among this type of firms that mostly focus on the PACAP rather than on the RACAP, some are deeply engaged in product and management innovations as opposed to process-related innovations (Solution 1). Other firms focus instead on the management and process innovations, at the expense of the improvement of existing products or the introduction of new ones. Thus, firms falling into this category can be “potential knowledge-based firms” (Table 4), since they are similar in their ACAP features. Conversely, Solutions 3 and 4 represent a second category of indistinguishable firms regarding to their managerial and non-managerial innovations, but that differ in their ACAP. More precisely, Solution 3 refers to firms in an intensive phase of assimilation and exploitation of the already acquired and transformed knowledge from external sources. In addition, Solution 4 indicates firms simultaneously engaged in an intensive phase of acquisition, assimilation, and transformation of external knowledge. Firms that may correspond to this second category can be similar in their innovation orientation. This category could be labeled as “innovation-based firms” (Table 4).

Table 4 Solutions table indicating configurations for achieving high organizational performance. Solution

4.4.2. fsQCA solution Table 4 presents the results of the fsQCA analysis of high organizational performance, with a specific representation according to the suggestion of Fiss (2011) and Ragin and Fiss (2008). Table 4 shows the existence of four causal paths leading to a high level of organizational performance. Each part of the overall solution, as well as the overall solution, has a consistency greater than or equal to 0.80, which is acceptable. Since these four solutions have a distinctive combination of conditions, they reflect what Fiss (2011) terms as a first-order equifinality. The fsQCA analysis reveals that the assimilation of new external knowledge and management innovation are two core conditions for all four configurations (Solutions). Thus, whatever the nature of the companies' ACAP and organizational innovation, their capabilities to assimilate new knowledge and manage innovation invariably has a strong causal relationship with organizational performance. The solutions in Table 4 can fit into two broad categories. Solutions 1 and 2 depict a first category of identical firms regarding their ACAP but that differ in their managerial and non-managerial innovation (only product and process innovation). In terms of ACAP, Solutions 1 and 2 are representative of firms with the capacity to acquire and assimilate new knowledge but which have not yet developed the skills for

Category of firms

“Potential knowledge based firms”

Configuration

1

2

3

4

● ● Ø Ø

● ● Ø Ø

Ø ● Ø ●

● ● ● Ø

● Ø ●

Ø • ●

● • ●

● • ●

Consistency Raw coverage Unique coverage

0.80 0.07 0.02

0.92 0.09 0.03

0.85 0.09 0.04

0.82 0.10 0.06

Overall solution consistency Overall solution coverage

0.84 0.23

Absorptive capacity Acquisition Assimilation Transformation Exploitation Organizational innovation Product innovation Process innovation Management innovation

“Innovation based firms”

Note: Black circles indicate the presence of a condition, and circles with “/” indicate its absence. In fsQCA, a condition is present (absent) when its degree of membership is higher (lower) to the crossover point (i.e., membership higher than 0.5). Large circles indicate core conditions; small ones, peripheral conditions. Blank spaces indicate “don't care”.

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Table 5 Complex configurations of absorptive capacity and organizational innovation indicating high level of organizational performance for the modeling subsample. Solution from the modeling subsample

Raw coverage

Unique coverage

Consistency

1. ~ac*as*ex*pdi*pri*mi 2. ac*as*tr*~ex*~pdi*~pri*mi 3. ac*as*~tr*~ex*pdi*~pri*mi 4. ac*as*~tr*ex*~pdi*pri*mi 5. ac*as*tr*~ex*pdi*pri*mi

0.15 0.08 0.09 0.09 0.13

0.11 0.03 0.04 0.04 0.07

0.83 0.82 0.84 0.91 0.86

Overall solution consistency: 0.85 Overall solution coverage: 0.39 Note: AC: Acquisition, AS: Assimilation, TR: Transformation, EX: Exploitation, PDI: Product innovation, PRI: Process innovation, MI: Management innovation, “~”: the negation of the condition.

This study concludes on the asymmetrical nature of the causal relationships leading to both high or low organizational performance. Overall, the fsQCA results provided in Tables 4 and 5 support H6.

4.4.3. Predictive validity of causal models The recent literature suggests a predictive validity of the fsQCA analysis (Gigerenzer & Brighton, 2009; Woodside, 2016). This study aims at testing the fit of data to theory. Table 5 highlights that the patterns of complex combination of conditions are causally consistent indicators of organizational performance's high levels. Furthermore, Fig. 2 shows that the first part of the modeling subsample's solution is causally relevant in predicting high levels of organizational performance, with a consistency almost equal to 0.80 (0.795). All results are available to any reader interested.

5. Discussion and conclusion Despite the literature suggesting a positive relationship between ACAP, organizational innovation, and performance, to date no research empirically analyzes these relationships in a holistic model. This study contributes to the literature by being the first to analyze each ACAP dimension's effect on organizational innovation (i.e., product, process, and management) and performance.

Tying in with Ali and Park (2016), this study's results support the hypothesized relationships, indicating that three of the four dimensions of ACAP (acquisition, assimilation, and exploitation) are key drivers of organizational innovation, which in turn increases organizational performance. However, the transformation dimension has no significant effect. This result does not imply that transformation is unimportant per se but, instead, suggests a less pronounced importance as compared to the other three ACAP dimensions. Bivariate correlations between transformation and the three innovation dimensions are all significant and positive (Table 2), supporting this dimension's absolute importance for organizational innovation (Hair et al., 2014). In addition, the relationship between product innovation and performance is not significant. One possible explanation may be that product innovation follows process innovation. Xu, Chen, and Guo (1998) argue that process innovation is more important than product innovation at the early stages of firm's innovation process. Relatedly, Camisón and Villar-López (2014) confirm that process innovation mediates the relationship between organizational innovation and product innovation. Additionally, some configurational arguments continue to fuel existing debates on the link between ACAP and organizational innovation and organizational performance. Especially, this reasoning deals with the sequentiality and/or complementarity ways within which the dimensions of ACAP (Todorova & Durisin, 2007; Zahra & George, 2002) and organizational innovation (Damanpour, 1991; Liao et al., 2007) intervene to explain organizational performance (Camisón & Villar-López, 2014; Jiménez-Jiménez & Sanz-Valle, 2011). Yet until now, no empirical studies examine which configurations of ACAP and organizational innovation yield a superior organizational performance. Using fsQCA, this study is a first approach to express configurations of ACAP and organizational innovation leading to organizational performance. The fsQCA results yield four configurations of ACAP and organizational innovation and differentiate two broad categories of firms that this study labels as “potential knowledge-based firms” and “innovationbased firms.” In the first category of firms, the breakdown of ACAP into PACAP and RACAP confirms the two-factor model of Zahra and George (2002). However, the second category confirms Jansen et al.’s (2005) four-factor model of ACAP. Overall, the fsQCA results further suggest that, within firms where the processing of knowledge is sequential (potential knowledge-based firms), organizational innovation variably intervenes in organizational performance. On the contrary, “innovation-based firms,” whose processing of knowledge is not sequential, are prone to the same organizational innovation orientation. Thus, the fsQCA analysis of this study contributes to the literature by providing an analytical insight of the configurational nature of organizational performance within an ACAP and innovation frame. This study highlights a major managerial implication, as companies wishing to engage in intensive innovation processes must first be aware of their ACAP. Furthermore, companies must not see the acquisition, assimilation, transformation, and exploitation of knowledge that make up this ACAP as cumulative. Instead, companies must design these dimensions in a sequentially or complementary manner. This study has several limitations, which warrant further investigation. First, although this study uses a well-validated scale for organizational performance, this measure is subjective in nature. Second, other configurations of various organizational factors such as regimes of appropriability, social integration mechanism, activation triggers, and power relationships (Todorova & Durisin, 2007; Zahra & George, 2002) could further contribute to the research framework of this study. Finally, future research could incorporate secondary sources of data, such as financial measures. References

Fig. 2. Test of the Part 1 of the solution from the modeling subsample using data from holdout subsample.

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Please cite this article as: Ali, M., et al., Direct and configurational paths of absorptive capacity and organizational innovation to successful organizational performance, Journal of Business Research (2016), http://dx.doi.org/10.1016/j.jbusres.2016.04.131

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Please cite this article as: Ali, M., et al., Direct and configurational paths of absorptive capacity and organizational innovation to successful organizational performance, Journal of Business Research (2016), http://dx.doi.org/10.1016/j.jbusres.2016.04.131