question

The mediating role of product and process innovations on the

relationship between knowledge management and operational performance in manufacturing

companies in Jordan Ahmad Fathi Al-Sa’di, Ayman Bahjat Abdallah and

Samer Eid Dahiyat Department of Business Management, The University of Jordan, Amman, Jordan

Abstract Purpose – The purpose of this paper is to investigate the effects of knowledge management (KM) on product and process innovations, as well as on operational performance (OP). In addition, the effects of product and process innovations on OP, as well as their mediating effects on the relationship between KM and OP, are also investigated. Design/methodology/approach – A questionnaire-based survey was designed and used to collect data from 207 manufacturing companies operating in the Jordanian capital Amman. To assess construct validity, exploratory and confirmatory factor analyses were conducted. To test research hypotheses, the bootstrap re-sampling method was applied using Hayes’s SPSS multiple-mediator PROCESS macro. Findings – The results indicate that KM has significant positive effects on product and process innovations, and OP. Process innovation was found to have a significant positive effect on OP, while product innovation was not. Furthermore, only process innovation was found to significantly mediate the KM-OP relationship. Practical implications – The findings of this study provide useful insights about the role of KM in facilitating and enhancing product and process innovations, as well as OP in the surveyed manufacturing companies. An important implication concerns the roles of product and process innovations. Manufacturing companies seeking improvements in their OP are recommended to focus on process innovation rather than product innovation. While product innovation may affect other aspects of performance, such as market and financial ones, it was not found to significantly affect OP. Process innovation can also leverage KM’s contribution to manufacturing companies’ OP. Originality/value – This is a pioneering study in that it developed an integrated model that depicts the interrelationships among KM, product innovation and process innovation and OP, in a developing country context. Keywords Knowledge management, Product innovation, Mediating effect, Process innovation, Bootstrapping, Operational performance Paper type Research paper

1. Introduction The current business environment affecting manufacturing organizations is characterized by intense competition, unprecedented technological developments, and volatile markets. Several factors have contributed to this situation most important of which are globalization, free trade agreements, advances in information and production technology, shortened product life cycles, and rapidly changing customer needs. As a consequence, manufacturing companies are increasingly pressured to better harness knowledge-based resources in a manner that enhances their operational performances and consequently sustains their competitiveness. In this context, knowledge management (KM) and innovation are seen as major strategic options, which can significantly enhance an organization’s ability to effectively respond to fickle customer requirements and changing technologies,

Business Process Management Journal

Vol. 23 No. 2, 2017 pp. 349-376

© Emerald Publishing Limited 1463-7154

DOI 10.1108/BPMJ-03-2016-0047

Received 4 March 2016 Revised 4 August 2016

Accepted 13 September 2016

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/1463-7154.htm

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thus maintaining their competitive performance in today’s turbulent business environment (Damanpour et al., 2009; Chen et al., 2010; Andreeva and Kianto, 2011; Dahiyat, 2015).

On one hand, the extant literature investigating the linkages between KM and performance mainly focuses on measuring performance in terms of organizational effectiveness (Gold et al., 2001), competitiveness (Liu et al., 2004), organizational performance (Lin and Kuo, 2007; Mazdeh and Hesamamiri, 2014), balanced scorecard (Lee and Lee, 2007), and market performance (Pérez-López and Alegre, 2012). However, an apparent gap exists in the literature concerning the contribution of KM to operational performance. Operational performance is a vital predictor of the effectiveness and efficiency of manufacturing companies and reflects the proficiency with which knowledge resources are managed and utilized for facilitating organizational product and process innovation efforts. Furthermore, a research gap exists concerning the expected direct and indirect effects of KM on such a performance (Tseng, 2008; López-Nicolás and Mero˜no-Cerdán, 2011).

On the other hand, the innovation-performance relationship has often been considered as ambiguous and one that is addressed by an extant literature characterized by contradicting and confusing results (Hashi and Stojcic, 2013), thus calling for the need to conduct further studies in order to investigate the linkages among different types of innovation and performance (Damanpour and Aravind, 2012). In particular, the effects of innovation types on the operational performance of manufacturing companies are under-investigated (Abdallah et al., 2016). Furthermore, there is a lack of studies that attempt to investigate the linkages among KM, innovation and operational performance (Choi et al., 2008) so as to delineate those types of innovation that play a crucial role in improving operational efficiency and enabling the organization to achieve its competitive priorities (Camison-Zornoza et al., 2004; Gunday et al., 2011).

Innovations occur as a result of incorporating new knowledge with existing knowledge to reconfigure organizational capabilities and competencies, resulting in value-added products. In this context, KM encompasses processes concerned with facilitating the creation and acquisition of new knowledge, integrating it with an organization’s existing repository of knowledge, sharing it and applying it in value-added outputs. As such, KM is argued to significantly enhance an organization’s innovation process (Cavusgil et al., 2003; Dahiyat and Al-Zu’bi, 2012; Dahiyat, 2015). While the theoretical literature emphasizes the crucial role of KM in facilitating innovation, empirical literature is still immature with mixed results and measures (Hall and Mairesse, 2006; Andreeva and Kianto, 2011). Specifically, the expected effect of KM on product and process innovations needs more empirical studies to explore and clarify those relationships (Darroch, 2005). Most of the published studies were conducted in developed countries. Manufacturers in developing countries such as Jordan have huge challenges to catch up with global competition. The current study contributes to the existing literature by investigating the proposed relationships in a developing country context, which is that of Jordan.

Based on this, the current study seeks to provide a two-fold contribution through empirically investigating the direct and indirect effects of KM on the operational performance of manufacturing companies, with product and process innovations as mediating variables. In addition, another contribution emanating from this study is the examination of the contributions of product and process innovations to operational performance. While there is a consensus among researchers that innovation is positively related to performance in general, existing literature lacks studies that link product and process innovations to operational performance in particular. The paper is structured as follows. Section 2 presents a review of relevant literature. Section 3 discusses research framework and hypotheses development. Section 4 discusses research methodology. Thereafter, data analysis and hypotheses testing are presented in Section 5. Section 6 presents a discussion of the results. Finally, conclusions, implications and research limitations are presented in Section 7.

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2. Literature review 2.1 KM KM could be viewed as a work process, an activity, a technology infrastructure or an operational culture to manage valuable corporate assets and knowledge (Chong et al., 2000; Pauleen et al., 2007). Kör and Maden, (2013, p. 2) defined KM as “business process which relates to creating new knowledge and ensuring usage of knowledge within organization whenever it is necessary.” KM has been assuming increased importance due to its role in reducing production cycle time and enhancing operating efficiency (Mishra and Bhaskar, 2011; Abdallah, 2013). Moreover, KM enables organizations to shorten their product development time, enhance employee productivity and performance, improve product quality and customer service, modernize and reengineer business processes, provide innovative products and services, and increase flexibility (Abdallah et al., 2009; Dahiyat and Al-Zu’bi, 2012; Slavković and Babić, 2013). KM also assists in achieving organizational goals by allowing know-how and expertise to be easily shared and accessed (Mishra and Bhaskar, 2011) as well as promoting the use of available sources of information, skills and experience (López-Nicolás and Mero˜no-Cerdán, 2011).

KM plays a significant role in facilitating an important process in organizations, namely, learning process. For example, effective KM could increase the amount of knowledge required for organizational members and facilitate the rapid diffusion of knowledge within the organization. Alavi and Leidner (2001) indicated that there is an agreement to treat KM as a group of processes that allow using knowledge as a key factor to add and generate value. There is generally a lack of agreement on the actual components or phases of KM. However, several researchers pointed to three main processes of KM, acquisition, sharing and application (e.g. Lin et al., 2012; Liao et al., 2011; Singh and Soltani, 2010; Zheng et al., 2010; Zaim et al., 2007; Alavi and Leidner, 2001). The first process in most KM models is knowledge acquisition through which the organization obtains knowledge from both internal and external sources (Uit Beijerse, 2000; Dahiyat and Al-Zu’bi, 2012). The second process is knowledge sharing, which is related to the transformation or throughput phase that includes disseminating, storing, codifying, and documenting knowledge (Wong and Aspinwall, 2005). The third process is knowledge application, which is considered as the output aspect of KM. Knowledge application is a focal element in KM process. According to the knowledge-based view, the real value of both individual and organizational knowledge exists when knowledge is applied because of implicitness of knowledge (Islam and Kulkarni, 2009). Lin and Lee (2005, p. 176) defined knowledge application as “the business processes through which effective storage and retrieval mechanisms enable a firm to access knowledge easily.”

2.2 Product innovation Product innovation is connected with both introducing new products and improving existing ones (Chang et al., 2012; Polder et al., 2010). Product innovation could include changes in design which, in turn, cause important changes in the use or features of a product (OECD, 2005). The main goal of having product innovations in an organization is to enhance the value delivered by the product and achieve a higher level of efficiency (Polder et al., 2010). In addition, product innovation can be achieved either by using new technologies and knowledge or by using new combinations of the existing technologies and knowledge (Gunday et al., 2011).

In general, product innovation is considered to be a difficult process since it is driven by changing customer needs, advancing technologies, increasing international competition and reducing product life cycles (Gunday et al., 2011). Product innovation is an ongoing and cross-functional process that involves and integrates an increasing number of different capabilities inside and outside the organizational limits. Product innovation provides

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manufacturers with the opportunity to keep their product portfolio competitive and consequently accomplish the competitive advantage they look for (Ottenbacher and Harrington, 2009). Despite all previous advantages of product innovation, it is still a risky and expensive attempt since the results show low success rates and many projects being ended midway in the development cycle (Cormican and O’Sullivan, 2004). In order to achieve organizational goals successfully, product innovation should have significant interactions within the organization as well as with customers and suppliers (Gunday et al., 2011).

A business that adopts responsive market orientation always attempts to put into its consideration the customers’ needs in their markets or segments; consequently it improves the services or products through innovation (Li et al., 2008). From an information-processing perspective, paying much attention to customers provides organizations with the ability to get more information about marketplace changes which plays a significant role in achieving successful product innovation (Zhang and Duan, 2010). Furthermore, responsive market orientation provides organizations with an opportunity to increase the reliability of information use and predictability of information search and to decrease the complexity of information application in the process of new product development (Atuahene-Gima et al., 2005).

Concentrating on the future needs of the customers provides organizations with a notification toward new market and technology developments; it also participates in raising the organization’s abilities in order to use these developments in product innovation. This will, consequently, participate in providing offerings with special benefits (Zhang and Duan, 2010).

2.3 Process innovation Process innovation has gained more importance recently (Trott and Hartmann, 2009; Van De Vrande et al., 2010; Lichtenthaler, 2011). It is defined as the application of a new or improved production or delivery methods which consist of important changes in techniques, equipment, and software (OECD, 2005). Process innovation enhances the efficiency and the productivity of production activities, increases quality and reduces unit cost of production (Abdallah and Phan, 2007). Process innovation involves either improvements in the production and logistic methods or improvements that include several activities such as accounting, computing, purchasing, and maintenance (Polder et al., 2010). Organizations that use process innovation aim at producing innovative products and new products as well (Hassan et al., 2013). This may require the adoption of new methods which have never been used before (Polder et al., 2010).

Damanpour (1991) pointed to two main stages of process innovation which included initiation and implementation. He asserted that initiation stage involves what is called “openness to the innovation” which is determined by the willingness of organizational members to adopt or resist innovation. Recent literature re-emphasized the importance of process innovation stages and reconfigured them (Lendel et al., 2015). These stages include identifying customer needs and innovation opportunities, search for new ideas, idea conversion, diffusion and generation (Hansen and Birkinshaw, 2007; Laursen and Salter, 2006; Bernstein and Singh, 2006). Another aspect is the creation a strong combination between internal and external sources to yield superior results (Krishnan and Jha, 2011). Throughout the process innovation, the way that an organization uses both knowledge and ideas of external partners is considered to be the core of the innovation (Laursen and Salter, 2006). It is important to establish an effective control system in order to evaluate deviations and failures of different stages of process innovation so that to assure successful implementation (Tidd and Bessant, 2011).

2.4 Operational performance Since the operations function plays a strategic role in building and sustaining competitiveness, manufacturing companies need to formulate operations strategies in a

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way that helps to implement their own corporate competitive strategies. Manufacturing competitive priorities are the ways in which an organization has the opportunity not only to choose to compete in the marketplace, but also to choose the type of markets it pursues (Mady, 2008). Operational performance can be defined as “the output or result achieved due to unique operational capabilities” (Tan et al., 2007, p. 5137). Manikas and Terry (2010) argued that operational performance can be considered as either internal performance or process performance. Flynn et al. (2010) referred to operational performance as the improvements in response of an organization to a changing competitive environment. According to Ketokivi and Schroeder (2003), operational performance is usually measured as a set of several dimensions that reflect the internal operations of an organization in terms of the elements of product, process quality, efficiency, and productivity. In some studies, operational performance was measured through productivity, effectiveness and efficiency of internal operations (e.g. Abdallah et al., 2014). However, the most widely used measures of operational performance in the literature are cost, quality, delivery, and flexibility (Abdallah et al., 2016; Al-Abdallah et al., 2014; Ortega et al., 2012; Phan et al., 2011; Flynn et al., 2010; Abdallah and Matsui, 2009). Our approach is to follow such widely used measures of operational performance using cost, quality, delivery, and flexibility.

3. Research model and hypotheses development In this paper, product and process innovations are posited to mediate the relationship between KM and OP. A positive effect of KM on both types of innovation as well as on OP is also hypothesized. Additionally, our research model assumes a positive effect of product and process innovations on OP. The resource-based view (RBV) of the firm (Wernerfelt, 1984) stands as the theoretical underpinning for our proposed model. RBV theory underlines the implications of internal resources to reach superior performance. Based on this theory, resources that are rare, valuable, difficult to substitute, and imperfectly imitable will contribute to sustainable performance and competitive advantage (Barney, 1991). We argue that KM capability enables manufacturing companies to introduce outstanding and exceptional innovations that will considerably enhance operational performance. The proposed study model is illustrated in Figure 1.

3.1 KM processes and product and process innovation In organizations, KM is considered to be an essential antecedent of innovation (Darroch and McNaughton, 2002; Nonaka and Takeuchi, 1995; Andreeva and Kianto, 2011; Dahiyat, 2015). Additionally, KM promotes engagement in innovations through developing new ideas and exploiting them an organization’s intellectual capital (Huang and Li, 2009; Plessis, 2007; Darroch and McNaughton, 2002). More specifically, externally generated knowledge acquisition

Knowledge Management Processes

Operational Performance

H4 Product

Innovation

Process Innovation

H1

H2 H5

H3

H6

H7

Figure 1. Research model

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gives individuals the opportunity to improve their abilities to develop new knowledge and to transform the available knowledge into new knowledge (Chen and Huang, 2009). Consequently, the new acquired knowledge contributes efficiently to maximize the available stocks of knowledge and minimize the uncertainty. As a result, the new obtained knowledge provides opportunities for creating innovative environment and thinking resulting in enhanced innovation (Lin and Lee, 2005; Dahiyat and Al-Zu’bi, 2012).

Knowledge sharing facilitates the process of exchanging knowledge, skills and experiences among employees, which leads to creating new routines and thinking models (Lin, 2007; Nonaka and Takeuchi, 1995). Additionally, sharing knowledge decreases the time and effort that employees need to gather information, which consequently transfers the organizational resources to be supportive to innovation processes. Moreover, sharing and exchanging knowledge contribute to learning and getting access to information of new knowledge, which is basic for the diffusion of innovative ideas (Chen and Huang, 2009).

Knowledge application is considered to be beneficial at two levels; first, it is related to the real use of current knowledge to solve problems (Gold et al., 2001). Second, it makes knowledge more active in establishing relevant values for an organization (Bhatt, 2001). Applying knowledge efficiently increases organizational ability to manage various sources of knowledge, decreases mistakes, and transforms collective knowledge to advantages for organizational innovative endeavors (Huang and Li, 2009; Alavi and Leidner, 2001; Bhatt, 2001; Gold et al., 2001). Subsequently, knowledge application is an important part of increasing product and process innovations in organizations (Sarin and McDermott, 2003). In fact, organizations would face serious problems without knowledge application; they would not be able to use the collective knowledge effectively in order to improve their innovation performance to the desired level (Alavi and Leidner, 2001). The increasing organizational interest in KM is attributed to the expected potential benefits of its application such as expanding the creativity of employees, generating creative ideas, and enhancing product and process innovations (Darroch, 2005; Borghini, 2005). As a result, innovation can be described as the most prominent result from KM (Majchrzak et al., 2004). Furthermore, it is argued that KM is not only important for creating new knowledge and innovation, but also for attaining the benefits based on innovation (Zack et al., 2009).

Several empirical studies have investigated the effects of KM processes on product and process innovations. Kör and Maden (2013) found that KM processes in Turkey have a significant positive effect on innovativeness, which in turn increases organizational innovation. Bas et al. (2015) empirically found that product innovation was significantly influenced by KM while process innovation is associated with workplace organization in Luxembourg. Donate and Sánchez de Pablo (2015) using a sample of technological companies from Spain found that KM significantly mediates the effect of knowledge-oriented leadership on product innovation. Andreeva and Kianto (2011) using a sample of 221 companies from Finland, Russia, and China concluded that KM processes positively affected innovation. Nielsen (2007) found that organizational practices related to learning and knowledge positively affected innovation and dynamic performance in Danish organizations. Lee et al. (2013) provided empirical evidence from Malaysian manufacturing companies concerning the effect of KM practices on technological innovation. They asserted that KM practices of knowledge sharing, knowledge application, and knowledge storage positively and significantly affected both product and process innovations. Islam et al. (2015) demonstrated a positive effect of KM on service innovation in academic libraries. Hence, the following hypotheses are proposed:

H1. KM has a significant effect on product innovation.

H2. KM has a significant effect on process innovation.

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3.2 KM and operational performance The importance of KM lies in its ability to provide new ways to accomplish explicit and implicit knowledge sharing. Sharing of intellectual assets represents a valuable source to enhance competitive advantage and organizational performance (Ndlela and du Toit, 2001). Moreover, organizations can maintain their competitiveness if they effectively create, acquire, document, transfer, and apply knowledge for solving problems and exploiting available opportunities (Sambamurthy and Subramani, 2005). Organizations with high levels of KM are usually more willing to learn how to improve their capabilities in responding to changes and are more successful in decreasing redundancy, developing creative ideas and improving overall performance (Lee et al., 2005). In addition, knowledge acquisition and sharing among individuals and organizational groupings impact the quality of decision making. Subsequently, it is highly important for organizations to involve their employees in KM processes in order to exploit knowledge and expertise to establish value and support organizational effectiveness (Scarbrough, 2003; Gold et al., 2001). Tseng and Lee (2014) argued that the success of KM program depends on its ability to affect organizational performance.

Several studies investigated KM-performance relationship. Performance in those studies was measured in different ways. For instance, Gold et al. (2001) demonstrated that KM capability positively affected organizational effectiveness. Liu et al. (2004) found a significant positive relationship between KM capability and competitiveness. Lin and Kuo (2007) concluded that KM capability was positively related to overall organizational performance including market performance and human resource performance. Ho (2008) found that KM capability directly related to financial performance. Similarly, Mohrman et al. (2003) found that knowledge was related to overall organizational performance including financial factors. Mazdeh and Hesamamiri (2014) using a sample of 254 organizations in North America found a significant effect of KM on the measures of organizational performance (financial, process, and internal). Lee and Lee (2007), using a sample collected from 68 KM-adopting Korean organizations, found significant relationships among KM capabilities, processes, and performance (in terms of balanced scorecard). Pérez-López and Alegre (2012) found that KM processes were directly related to market performance and indirectly related to financial performance in Spain:

H3. KM has a significant direct effect on operational performance.

3.3 Product and process innovations and operational performance Innovations are crucial conditions for improving performance and raising organizational value (Llore´ns Montes et al., 2005; Bowen et al., 2010). Thus, innovative organizations show a higher level of economic growth and productivity than non-innovative ones (Cainelli et al., 2004). Organizations achieve excellence in operational performance dimensions such as cost, quality, delivery, and flexibility as a result of focusing their resources and efforts on product and process improvements and innovations (Tan et al., 2007).

Several empirical studies showed a strong positive relationship between innovation and performance. Kafetzopoulos and Psomas (2015) found that the level of innovativeness was positively related to productivity and performance. Hassan et al. (2013) concluded that innovations (product and process) were positively related to production performance due to new operational and business methods applied. Similarly, Saunila et al. (2014) demonstrated that organizations which are more successful in innovations had higher operational and financial performance than others. Evangelista and Vezzani (2010) indicated that product innovation provides organizations with operational benefits by using novel technology to enhance product performance. They further showed that process innovation improves performance through efficiency-productivity gains acquired by introducing more effective

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ways of production which leads to reduced response time, improved quality, and reduced costs. Ou et al. (2010) asserted that process innovation effectively improves internal production operations resulting in decreased cost and improved operational performance. Moreover, product innovation enhances the ability to respond to changes effectively by developing new capacities that lead to improved operational performance Lloréns-Montes et al. (2004). Thus, we propose the following hypotheses:

H4. Product innovation has a significant effect on operational performance.

H5. Process innovation has a significant effect on operational performance.

3.4 The mediating effects of product and process innovations on the relationship between KM and operational performance The role of KM in enhancing operational performance has been discussed in Section 3.2. This positive effect is supported by various arguments in the literature. Additionally, KM enables organizations to innovate in both products and processes. The effects of these innovation types on operational performance have been widely discussed in the literature. This study argues that the effect of KM on operational performance will be greater in organizations involved in product and process innovations. This means that in addition to the direct effect of KM on operational performance, an indirect effect exists through the two types of innovation. This indirect effect exists due to real exploitation of organizational resources (Lin and Kuo, 2007).

Knowledge capability provides organizations with the ability to design efficient and innovative processes that contribute to improving quality, flexibility and delivery and reducing cost. Process innovation affects operational performance by improving production processes and production efficiency (Damanpour and Gopalakrishnan, 2001). Moreover, from a RBV (Wernerfelt, 1984; Barney, 1991), process innovation provides organizations with competitive advantage that cannot be easily imitated if the knowledge on which this innovation is based is exclusive. Schiuma and Carlucci (2008) indicated that KM enables companies to establish capacity to innovate and, consequently, to improve operational and organizational performance.

Today’s dynamic markets force manufacturers to continually improve their flexibility and responses to customers. Those competencies require effective KM that facilitates the transformation of organizational resources into capabilities and organizational competencies in terms of improved performance outcomes (Darroch, 2005; Chang and Ahn, 2005). KM leads to increased innovation and creativity in products and processes, which, in turn, results in improved operational performance. Increased process innovation contributes to operational performance by reducing production costs, improving current production processes, and improving productivity and efficiency of the plant (Fritsch and Meschede, 2001; Ofek and Sarvary, 2001). Enhanced product innovation results in improved product quality, enhanced technological improvements and the creation of new products with higher performance (Chang and Ahn, 2005). Furthermore, effective KM indirectly affects operational performance through product and process innovations by enabling manufacturing companies to concentrate on value adding activities depending on the innovation type, whether it is concerned with products or processes (Inkinen et al., 2015).

A number of studies addressed the role of innovation in KM-performance relationship. Ruiz-Jiménez and Fuentes-Fuentes (2013) explored the impact of product and process innovations on the relationship between knowledge combination capability and organizational performance in Spanish SMEs. They found that knowledge combination capability greatly affected product and process innovations. They also found a significant mediating effect of both types of innovation on the relationship between knowledge

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combination capability and organizational performance. Slavković and Babić (2013) found that KM positively affected organizational performance using a sample of leading Serbian companies. They also empirically demonstrated that KM positively affected administrative and process innovations. Furthermore, they found significant mediating effects of both process and administrative innovations on the relationship between KM and organizational performance. Naghavi et al. (2012) using a sample of Iranian public sector organizations found that KM processes positively affected organizational performance and organizational innovation. They also found a mediating effect of organizational innovation on the relationship between KM and performance. Mafabi et al. (2012) investigated the effect of KM on innovation and organizational flexibility in Ugandan parastatal organizations. They found that KM significantly affected innovation and insignificantly affected flexibility. They also found a full mediation effect of innovation on the relationship between KM and flexibility. López-Nicolás and Mero˜no-Cerdán (2011) using a sample of 310 Spanish organizations concluded that two KM strategies, codification and personalization directly affected corporate financial and internal performance and indirectly through innovation.

Although the mediating effects of product and process innovations on KM-operational performance have not been explicitly investigated in the literature, we build on the above arguments and propose the following hypotheses:

H6. Product innovation positively mediates the relationship between KM and operational performance.

H7. Process innovation positively mediates the relationship between KM and operational performance.

4. Methodology 4.1 Data collection and sample The population for this study consisted of all manufacturing companies in the capital of Jordan, the city of Amman. According to Amman Chamber of Industry (ACI, 2015), the number of manufacturing companies in Amman is 1,200. The suggested sample size for this population is 292 (Sekaran and Bougie, 2010). In an attempt to get this sample size, 300 questionnaires were distributed by the researchers using personal visits to manufacturing companies to ensure high response rate. Usually questionnaires sent by mail or e-mail are neglected in Jordan; therefore, we selected the personal visits approach. Types of visited manufacturing companies included chemical, electrical and electronics, pharmaceutical, machinery and mechanical appliances, and others. Our approach was to conduct plant level analysis. Therefore, we targeted one respondent from each manufacturing company. We targeted managers in the top or middle levels with responsibilities related to KM and innovation activities. Those managers included executive managers, operations managers, plant managers, departmental managers, and others. The data collection process lasted for two months during June and July 2015. In total, 216 questionnaires were filled out by respondents. Nine questionnaires were excluded due to missing data or other problems leaving a total of 207 valid questionnaires for subsequent data analysis representing a response rate of 69 percent. This response rate is higher than other studies in Jordan that used personal visits approach. For instance Obeidat et al. (2014) showed a response rate of 52 percent and Suifan et al. (2015) received a response rate of 64.3 percent.

4.2 Measurement scale assessment The items used to measure research constructs were adopted from the literature. The items to measure KM processes were adopted from Pe´rez-Lo´pez and Alegre (2012), items to measure product and process innovation were adopted from Gunday et al. (2011), and items to measure operational performance were adopted from Flynn et al. (2010).

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The adopted measurement scales were consistent with the operational definitions of the constructs used in this research. KM is operationally defined as the explicit and systematic management of vital knowledge, and its associated processes of acquisition, sharing, and application, in pursuit of business objectives (Alavi and Leidner, 2001). Product innovation is defined as any new or significantly improved products that are provided by the organization for the customers’ benefit (Alegre et al., 2006). Process innovation is defined as changes and improvements in processes that aim at improving productivity, efficiency and effectiveness of production activities (Kim et al., 2012). Operational performance refers to the performance of internal operations of an organization in terms of a combination of several performance dimensions that include cost, quality, delivery, and flexibility (Ketokivi and Schroeder, 2003).

The measurement scales were translated into Arabic to avoid any misunderstanding. The scales were reviewed by five professors in business administration and were revised as needed. Respondents were asked to evaluate their agreement or disagreement with the statements provided using five-point Likert scales were 5 indicated strongly agree and 1 indicated strongly disagree.

4.3 Validity and reliability The face and content validity of the research instrument were assessed through a pilot study phase, which included five professors specialized in the areas of KM and operations management in the University of Jordan, School of Business. Additionally, the opinions of four managers working in two manufacturing companies operating in Jordan were sought to evaluate the clarity and relevance of the questionnaire items, and the instrument items were revised accordingly. The content validity of the developed questionnaire was assured by thoroughly examining relevant empirical and theoretical studies related to the main research constructs, including: KM process capabilities, technical innovation, as well as operations management performance (see Section 4.2).

With regard to construct validity, as recommended by Hair et al. (2010), exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to assess construct validity. Thus, EFA was performed to identify whether items measuring each constructs loaded onto one or more factors or dimensions. Moreover, CFA, derived from structural equation modeling (SEM), was also utilized to confirm or refine the unidimensionality of measurements that resulted from the EFA, since it is a more rigorous test of unidimensionality (Garver and Mentzer, 1999). To assess the EFA, four commonly used assumptions were followed (Hair et al., 2010; Field, 2000): sampling adequacy (Kaiser-Meyer-Olkin measure greater than 0.5) and Bartlett’s test of sphericity to test homogeneity of variances statistics were statistically significant ( po0.05); the minimum eigenvalue for each factor to be one; considering the sample size, factor loading of 0.40 for each item was considered as the threshold for retaining items to ensure greater confidence; and varimax rotation was used since it is a good general approach that simplifies the interpretations of factors (Field, 2000). To assess the CFA, goodness of measurement model fit using SEM were followed (Chau, 1997): χ2 ( p⩾0.05); goodness-of-fit index (GFI ⩾ 0.90); adjusted goodness-of-fit index (AGF ⩾ 0.80); normed fit index (NFI ⩾ 0.90); non-normed fit index (NNFI ⩾ 0.90); comparative fit index (CFI ⩾ 0.90); standardized root mean-square residual (SRMR⩽0.08); and root mean square error of approximation (RMSEAo0.10).

EFA results showed that KMO statistic for all scales was greater than 0.50 and Bartlett’s test of sphericity statistics were statistically significant (po0.05) implying the appropriateness of factor analysis. The results of EFA revealed that all KM items loaded onto one factor except for two items which loaded onto two factors and were deleted. This implied that we could only use KM as one construct in our subsequent analysis. Question items related to innovation types

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loaded onto two factors representing product and process innovation. Also, operational performance items loaded onto one factor (see Tables I, II, and IV).

To confirm and validate the findings that emerged from using EFA, the factors emerging from EFA measuring KM processes, product and process innovations, as well as operational performance were evaluated by CFA using EQS 6.1 software. Tables I, II, and IV show the results and a summary of the goodness-of-fit indices for each respective CFA model, which were all met. It should be noted that there were non-significant loadings; this is due to the measurement model identification. The parameters without (*) in all table contents are specified as starting values “specified as fixed.” A starting value is needed for each of the parameters’ constructs to be estimated because the fitting algorithm involves iterative estimation, starting from a suitable approximation to the required results and proceeding to their “optimum” values (Dunn et al., 1994). As the aforementioned tables show, all items loadings emerging from CFA well exceeded the cut-off point value; 0.40. Items with non-significant factor loadings, high measurement errors and low factor loadings as compared with the suggested 0.40 threshold were deleted (Hair et al., 2010). Also, certain items were deleted since this significantly improved the model’s goodness-of-fit, and was also sound theoretically. Consequently, two items (KM5, KM9) were deleted from “Knowledge Management,” two items (PRDIN2, PRCIN1) were deleted from “Process Innovation,” one item (PRDIN1) was deleted from “Product Innovation,” and one item (OPERF4) was deleted from “Operational Performance”.

Convergent validity is examined by using the Bentler-Bonett NFI (Bentler and Bonett, 1990). As shown in Tables I, III, and IV, all CFA models have an NFI value that equals or is above 0.90. Furthermore, indication of the measures’ convergent validity is provided by the fact that all factor loadings are significant and that the scales exhibit high levels of internal consistency (Gerbing and Anderson, 1988). Also, as shown in Table IV, the values of composite reliability and average variance extracted for each construct were all above the threshold: 0.60 and 0.50, respectively.

5. Results This section reports the results of the empirical analysis to test research hypotheses. Means, standard deviations, reliabilities, and correlations among variables are provided in Table V. Highest correlations existed between KM with both innovation types as well as with operational performance. High correlation also existed between process innovation and operational performance. High correlations usually raise concerns about multicollinearity. Variance inflation factor (VIF) and tolerance values were reviewed for KM and both types of innovation entered together into a regression model with operational performance as a dependent variable. The results showed that the highest VIF value was 2.1 (tolerance value is 0.45) indicating that multicollinearity was not a concern in our model (Hair et al., 2010).

To test the research hypotheses, the bootstrap re-sampling method was used (Shrout and Bolger, 2002). This method has become very popular in testing mediation effects due to its superiority over other methods such as the one described by Baron and Kenny (1986). The normal theory approach proposed by Baron and Kenny (1986) and the associated Sobel test assume that the indirect effect is normally distributed; however, this assumption is very suspicious especially when sample size is not large enough (Hayes et al., 2011; Hayes, 2009; Preacher and Hayes, 2008; MacKinnon, 2008; Mallinckrodt et al., 2006). Furthermore, it has been proved that bootstrapping method is superior to product of coefficients, distribution of product, and the causal steps method in terms of power and type I error rates (MacKinnon et al., 2004).

The multiple-mediator model in this study is tested using the approach described by Hayes (2009) and Hayes et al. (2011). The 95 percent bias-corrected confidence intervals were generated using 5,000 bootstrap samples from the original data set. Hayes’s SPSS multiple-mediator PROCESS macro was used to test for direct and indirect effects (Hayes, 2013).

359

Mediating role of product and

process

E F A

re su lt s

C F A

re su lt s

It em

s F ac to r

lo ad in gs

Sa m pl in g ad eq ua cy

(K M O m ea su re )a nd

si g.

E ig en va lu e an d

(% of

va ri an ce )

F ac to r

lo ad in gs

C om

po si te

re lia bi lit y

A ve ra ge

va ri an ce

ex tr ac te d

K M 1: w e re gu

la rl y m ee t w it h ou r cu st om

er s in

or de r to

fi nd

ou t

w ha t th ei r ne ed s w ill

be in

th e fu tu re

0. 63 5

0. 89 5 si g ¼ 0. 00 0

5. 69 8 (4 7. 48 4%

) 0. 60

0. 85

0. 45

K M 2: ou r fi rm

ha s pr oc es se s fo r ac qu

ir in g kn

ow le dg

e ab ou t ou r

su pp

lie rs

0. 73 4

0. 71

K M 3: w e ha ve

a sy st em

th at

al lo w s us

to le ar n su cc es sf ul

pr ac ti ce s fr om

ot he r or ga ni za ti on s

0. 66 0

0. 60

K M 4: w e ha ve

pr oc es se s fo r ge ne ra ti ng

ne w

kn ow

le dg

e fr om

ex is ti ng

kn ow

le dg

e 0. 74 8

0. 72

K M 5: ne w

id ea s an d ap pr oa ch es

on w or k pe rf or m an ce

ar e

ex pe ri m en te d co nt in uo us ly

0. 63 5

D el et ed

K M 6: m ee ti ng

s ar e pe ri od ic al ly

he ld

to in fo rm

al lt he

em pl oy ee s

ab ou t th e la te st

in no va ti on s in

th e co m pa ny

0. 69 3

0. 65

K M 7: th e co m pa ny

ha s fo rm

al m ec ha ni sm

s to

gu ar an te e th e

sh ar in g of be st pr ac ti ce s am

on g th e di ff er en t de pa rt m en ts

0. 72 7

0. 70

K M 8: th er e ar e in di vi du

al s in

th e or ga ni za ti on

w ho

pa rt ic ip at e

in se ve ra l te am

s or

di vi si on s an d w ho

al so

ac t as

lin ks

am on g th em

0. 69 3

0. 66

K M 9: th er e ar e in di vi du

al s re sp on si bl e fo r co lle ct in g an d

in te rn al ly

di ss em

in at in g em

pl oy ee s’ su gg

es ti on s

0. 60 7

D el et ed

K M 10 :c om

pa ny

’s sy st em

s an d pr oc ed ur es

ar e fl ex ib le en ou gh

to al lo w fo r im

m ed ia te m od if ic at io ns

to be

m ad e on

ho w

to ap pl y ne w

kn ow

le dg

e

0. 66 9

0. 63

K M 11 :m

an ag em

en t em

ph as iz es

th e im

po rt an ce

an d

si gn

if ic an ce

of ut ili zi ng

ne w

kn ow

le dg

e 0. 74 8

0. 73

K M 12 :o ur

fi rm

is ab le to

lo ca te an d ap pl y kn

ow le dg

e ne ed ed

to en ha nc e it s co m pe ti ti ve ne ss

0. 70 3

0. 66

C F A

m od el go od ne ss -o f- fi t in di ce s re su lt s

χ2 N F I

N N F I

C F I

G F I

A G F I

SR M R

R M SE

A 10 2. 59 0,

p ¼ 0. 00 0

0. 90

0. 90

0. 92

0. 92

0. 87

0. 05

0. 09

Table I. Exploratory (EFA) and confirmatory factor analyses (CFA) for the knowledge management construct

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In order to accept or reject the hypotheses concerning the indirect effects, lower and upper bounds of confidence intervals related to each indirect effect has to be checked. If zero is contained between the lower and upper bounds, then the hypothesis concerning the indirect effect is rejected because this implies that the indirect effect is zero with 95 percent confidence. If the lower and upper bounds do not contain zero, then the hypothesis concerning the indirect effect is accepted because there is a 95 percent confidence that the indirect effect is not zero. Table VI shows the results of all direct and indirect effects that allow us to test all our hypotheses. The results show that KM positively and significantly affects product innovation ( β ¼ 0.844, po0.001) and process innovation ( β ¼ 0.483, po0.001); therefore, hypotheses H1 and H2 are supported.

As for hypothesis H3 which stated that KM positively and directly affects operational performance, the results show that the direct effect of IV on DV is positive and significant ( β ¼ 0.275, po0.001); therefore, hypothesis H3 is also supported.

Concerning hypotheses H4 and H5 which stated that product and process innovations positively related to operational performance (mediators on DV), the results show that product innovation is insignificantly related to operational performance ( β ¼ 0.021, pW0.05) while process innovation is positively and significantly related to operational performance ( β ¼ 0.316, po0.001). Based on these results, hypothesis H4 is not supported while hypothesis H5 is supported.

Next, hypotheses H6 and H7 concerning the mediating effects of product and process innovations on KM-OP relationship are tested.

Process innovation

Product innovation

Items Factor loadings Factor loadings

PRDIN1: our company regularly increases the quality of the components and materials used in manufacturing our current products 0.701

PRDIN2: our company regularly decreases the cost of the components and materials used in manufacturing our current products 0.625

PRDIN3: our company improves and adds new features to its current products in order to enhance their ease of use and improve customer satisfaction 0.683

PRDIN4: our company develops new products with technical specifications and functionalities that are different from the existing ones 0.845

PRDIN5: our company develops new products that include new components and materials that are different from what is currently being used 0.759

PRCIN1: our company determines and eliminates non-value adding activities in its production processes 0.617

PRCIN2: our company regularly decreases the costs associated with its manufacturing processes, techniques, machinery, and software 0.739

PRCIN3: our company regularly increases the quality of its manufacturing processes, techniques, machinery and software 0.684

PRCIN4: our company determines and eliminates non-value adding activities in its product delivery processes 0.694

PRCIN5: our company regularly decreases the cost associated with its product delivery and logistics processes 0.480 0.411

PRCIN6: our company regularly increases the delivery and logistics speed related to its products 0.466 0.455

Sampling adequacy (KMO measure) and sig. 0.815 (sig. ¼ 0.000) Eigenvalue and (% of variance) 4.106 (37.326%) 1.510 (13.727%)

Table II. Exploratory factor analysis (EFA) for

product and process innovation items

361

Mediating role of product and

process

EFA CFA

Factor loadings

Factor loadings

Composite reliability

Average variance extracted

Process innovation PRDIN2: our company regularly

decreases the cost of the components and materials used in manufacturing our current products

0.625 Deleted 0.61 0.41

PRCIN1: our company determines and eliminates non-value adding activities in its production processes

0.617 Deleted

PRCIN2: our company regularly decreases the costs associated with its manufacturing processes, techniques, machinery, and software

0.739 0.45

PRCIN3: our company regularly increases the quality of its manufacturing processes, techniques, machinery and software

0.684 0.80

PRCIN4: our company determines and eliminates non-value adding activities in its product manufacturing processes

0.694 0.63

Product innovation PRDIN1: our company regularly

increases the quality of the components and materials used in manufacturing our current products

0.701 Deleted 0.71 0.56

PRDIN3: our company improves and adds new features to its current products in order to enhance their ease of use and improve customer satisfaction

0.683 0.60

PRDIN4: our company develops new products with technical specifications and functionalities that are different from the existing ones

0.845 0.92

PRDIN5: our company develops new products that include new components and materials that are different from what is currently being used

0.759 0.68

CFA model goodness-of-fit indices results

χ2 NFI NNFI CFI GFI AGFI SRMR RMSEA

14.69, p ¼ 0.06

0.95 0.96 0.98 0.98 0.94 0.05 0.06

Table III. Confirmatory factor analysis (CFA) for the product and process innovation items

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E F A

re su lt s

C F A

re su lt s

It em

s F ac to r

lo ad in gs

Sa m pl in g ad eq ua cy

(K M O

m ea su re ) an d si g.

E ig en va lu e an d (%

of va ri an ce )

F ac to r

lo ad in gs

C om

po si te

re lia bi lit y

A ve ra ge

va ri an ce

ex tr ac te d

O P E R F 1: ou r co m pa ny

is kn

ow n fo r it s

ex ce lle nt

on -t im

e de liv

er y

pe rf or m an ce

0. 79 1

0. 67 7 si g ¼ 0. 00 0

1. 95 9 (4 8. 97 7%

) 0. 64

0. 62

0. 42

O P E R F 2: th e le ad

ti m e fo r fu lf ill in g

cu st om

er s’ or de rs

(t he

ti m e

be tw

ee n th e re ce ip t of

cu st om

er ’s or de r an d th ei r

fu lf ill m en t) is sh or t co m pa re d

w it h ou r m ai n co m pe ti to rs

0. 80 4

0. 76

O P E R F 3: ou r co m pa ny

’s pr od uc ts

co nf or m

to pr e- de te rm

in ed

sp ec if ic at io ns

0. 65 4

0. 52

O P E R F 4: th e co m pa ny

’s co st

of m an uf ac tu ri ng

pe r un

it is le ss

th an

th at

of ou r m ai n

co m pe ti to rs

0. 50 9

D el et ed

C F A

m od el go od ne ss -o f- fi t

in di ce s: de si re d le ve l

χ2

p⩾ 0. 05

N F I⩾ 0. 90

N N F I⩾ 0. 90

C F I⩾ 0. 90

G F I⩾ 0. 90

A G F I⩾ 0. 80

SR M R ⩽ 0. 08

R M SE

A ⩽ 0. 10

M od el in di ce s re su lt s:

E xc el le nt

m od el fi t; N F I is 1

Table IV. Exploratory (EFA) and confirmatory

factor analyses (CFA) for the operational

performance construct

363

Mediating role of product and

process

Confidence intervals for product innovation range between −0.048 and 0.109. As these values contain a zero, the indirect mediating effect of product innovation is insignificant and hypothesis H6 is not supported. Confidence intervals for process innovation range between 0.048 and 0.247. These values do not contain a zero which indicate that the mediating effect of process innovation is significant; therefore hypothesis H7 is supported. Figure 2 summarizes the tested relationships.

6. Discussion The results of the statistical analysis revealed a significant effect of KM processes on product innovation. From a theoretical perspective, these results showed that the KM processes are effective for increasing innovative products and enhancing the ability of manufacturing companies to compete in new and different markets. These results are consistent with previous studies (e.g. Chen and Huang, 2009; Lin and Lee, 2005; Sarin and McDermott, 2003; Gold et al., 2001; Bhatt, 2001). This finding asserts that, in manufacturing companies’ context, KM processes are essential factors to enhance product innovation.

The results showed a significant effect of KM processes on process innovation. This confirms previous literature that asserted the essential role of KM to improve processes, reduce production costs, and improve quality of the products which ultimately lead to sustainable competitive advantage (e.g. Ruiz-Jiménez and Fuentes-Fuentes, 2013; Slavković and Babić, 2013; Mafabi et al., 2012; Lee and Choi, 2000).

Mean SD Cronbach’s α 1 2 3

1. Knowledge management 3.50 0.882 0.866 1 2. Product innovation 3.53 1.039 0.760 0.677* 1 3. Process innovation 3.63 0.845 0.653 0.489* 0.316* 1 4. Operational performance 3.92 0.819 0.673 0.448* 0.326* 0.472* Note: *p ⩽ 0.01

Table V. Means, standard deviations, and correlations among study constructs

Bias corrected bootstrap 95% confidence interval

Model R2 Path coefficient SE Lower Upper

IV on mediators (a paths) KM → Product innovation (a1) 0.472 0.844* 0.062 0.722 0.967 KM → Process innovation (a2) 0.234 0.483* 0.061 0.363 0.604 Mediators on DV (b paths) 0.292 Product innovation → OP (b1) 0.021 0.064 −0.104 0.148 process innovation → OP (b2) 0.316* 0.065 0.187 0.444

Total effect of IV on DV (c path) KM → OP 0.212 0.446* 0.060 0.328 0.565

Direct effect of IV on DV (c’ path) KM → OP 0.275* 0.085 0.107 0.444

Indirect effects of IV on DV Total 0.171 0.059 0.052 0.288 Product innovation 0.018 0.048 −0.048 0.109 Process innovation 0.152 0.040 0.048 0.247 Notes: Based on 5,000 bootstrap samples; *po0.001

Table VI. Regression results for multiple mediation analyses

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KM plays a significant role in determining the speed that a product needs to reach the market and updating internal processes (Sarin and McDermott, 2003). Applying the acquired knowledge effectively by either individual employees or teams will decrease the number of mistakes and the decisions will be made more quickly and, thus, leading to improved process innovation. The flow of knowledge used in the process innovation is guaranteed by KM processes. By providing KM processes, knowledge that is necessary for process innovation has the ability to flow easily across functional and organizational boundaries to enhance both external and internal cooperation (Plessis, 2007).

The results also revealed a significant relationship between KM processes and operational performance. This result is consistent with previous studies that found a positive effect of KM on some performance measures (Chang and Chuang, 2011; Anderson, 2009; Zack et al., 2009; Liao and Wu, 2009; Asoh et al., 2007; Lee and Lee, 2007; Darroch, 2005, Gold et al., 2001). The results emphasized the essential role of KM to enhance operational performance. The effective application of knowledge increases the organizational ability to manage various sources of knowledge and transform collective knowledge to advantages for improving performance. When a company effectively acquires, shares, and applies the knowledge, it will be able to use its resources in a better way with higher efficiency and effectiveness resulting in improved operational performance.

The results showed an insignificant effect of product innovation on operational performance. This result is inconsistent with some previous studies (Marodin and Saurin, 2015; Hassan et al., 2013; Gunday et al., 2011) which asserted that product innovation has a positive impact on operational performance. In an ideal situation, the delivery of new innovative products can be accompanied with improvements in operational performance. However, in many cases, new products are associated with new processes, technologies, and new production techniques. Such a situation may increase variations in production processes which may potentially decrease operational performance, or at least keep it constant.

KM

Process Innovation

Product Innovation

OP

OPKM

a2 = 0.483*

a1= 0.844* b1= 0.021

b2 = 0.316*

c = 0.446*

c ′= 0.275*

Notes: Indirect effect for product innovation =a1·b1= (0.844)(0.021) = 0.018; indirect effect for process innovation =a2·b2 = (0.483)(0.316) = 0.152. *p< 0.001

Figure 2. A single step multiple- mediator model with

two proposed mediators

365

Mediating role of product and

process

Process innovation proved to have a significant effect on operational performance. This finding indicates that new production techniques and procedures that result in higher efficiency and productivity are expected to enhance internal processes and manufacturing performance. This result is consistent with previous literature which asserted that process innovation positively affects operational performance of manufacturing companies (e.g. Kafetzopoulos and Psomas, 2015; Saunila et al., 2014; Löfsten, 2014; Atalay et al., 2013). In addition, the results show that process innovation has a positive impact on operational performance while product innovation has not. Companies seeking improvements in their operational performance are recommended to implement process innovation so that to enhance their competitive advantage. While product innovation may affect market and financial performances, no empirical evidence was found concerning its effect on operational performance.

The results proved the existence of a significant mediating effect of process innovation on the relationship between KM and operational performance. While the direct effect of KM on operational performance was found positive and significant, the full value of this relationship can be realized through process innovation.

This result is consistent with previous studies (Paananen, 2009; Schiuma and Carlucci, 2008; Schiuma and Lerro, 2008; Damanpour and Gopalakrishnan, 2001). As mentioned earlier, KM provides organizations with the ability to manage their capacities and resources. If organizations exploit this knowledge to enhance process innovation as well, they will get greater benefits of KM processes as this knowledge provides organizations with more sustainable competitive advantages (Darroch, 2005). Process innovation is considered among the main sources of creating a sustainable competitive advantage and in order to achieve this strategic endeavor, an effective KM strategy should be pursued.

The results revealed an insignificant mediating effect of product innovation on the relationship between KM and operational performance. The results showed that product innovation neither directly affects operational performance nor mediates KM-OP relationship. Companies that attempt to improve operational performance are not recommended to focus on developing new products. Different benefits are expected to be gained by pursuing product innovation strategy, but as indicated by our results, operational performance is not among those benefits. Previous studies found a significant mediating effect of product innovation on KM-organizational performance relationship (Paananen, 2009; Schiuma and Carlucci, 2008; Schiuma and Lerro, 2008; Damanpour and Gopalakrishnan, 2001). It should be noted that organizational performance in those studies consisted mainly of financial performance, market performance, and customer satisfaction. Thus, it is highly important for manufacturing companies to focus on process side of innovation in order to enhance the engagement of employees in manufacturing process flow and improve operational performance.

7. Conclusions, implications, and limitations 7.1 Conclusions In this study, a theoretical framework has been developed to investigate the effects between KM processes on operational performance directly and indirectly through product and process innovations in Jordanian manufacturing companies. On the basis of this research, the following conclusions were drawn.

The findings revealed that KM has a significant positive impact on both types of innovation, product and process. Manufacturing companies considering a competitive strategy based on innovation are strongly recommended to initiate KM program as a key pillar and major enabler of innovation.

The research findings indicated that process innovation positively affected operational performance while product innovation did not. Companies aiming at improving their

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operational performance have to focus on process innovation which contributes to quality improvements, cost reduction and response upgrading. Product innovation may bring various benefits other than improvements in operational performance.

The results also showed a positive direct effect of KM on operational performance. By having a sound KM program, manufacturing companies will have an accumulated pool of valuable knowledge and information obtained from internal and external sources and organized in such a way that makes them accessible and value adding to operational performance and competitive advantage.

Finally, the findings revealed a positive mediating effect of process innovation on KM-operational performance relationship. The full potential benefits of KM on operational performance are enabled through process innovation. While imitation brings some benefits to performance, process innovation contributes to sustainable competitive advantage by providing manufacturing companies with competencies that would be difficult for competitors to imitate. This is in consonance with the RBV theory of the firm (Wernerfelt, 1984) which affirmed the role of internal capabilities and competences in creating a sustainable competitive advantage.

7.2 Managerial implications The findings of the current study carry important managerial implications. Managers in manufacturing companies should place extra emphasis on KM processes when considering both product and process innovations. Knowledge acquired from external sources such as customers, suppliers, and other organizations is a valuable source of innovations. Additionally, the internal ability to generate new knowledge from existing knowledge by having a systematic approach to collect employee suggestions and ideas, accompanied with flexible procedures to share and apply new knowledge, will boost innovation capability in both products and processes. Moreover, KM processes are of crucial importance for improving operational performance of manufacturing companies. KM enhances organizational ability to reduce manufacturing cost per unit, improve product quality, shorten delivery time, and reduce lead time. Managers should understand that merely having knowledge does not guarantee higher innovation and operational performance levels. Managers should reinforce KM processes among all functions and members of the company so that knowledge is effectively shared, disseminated and applied.

Managers should put additional emphasis on the expected contribution of innovation types to operational performance. The strategic choice of the appropriate innovation type enhances the achievement of overall organizational goals. To achieve better operational performance, managers are recommended to adopt process innovation rather than product innovation. Process innovation enables companies to reduce the costs associated with their manufacturing processes and improve their quality through eliminating non-value adding activities in these processes. While some studies linked product innovation to other performance dimensions such as financial performance, market share and sales growth, the current study demonstrated that product innovation does not contribute to operational performance of manufacturing companies. Companies that focus on product innovation to improve their operational performance may sacrifice their resources and efforts without achieving desirable performance outcomes. Having a clear grasp of the expected contribution of each innovation type to operational performance will assist manufacturing companies to prioritize their expenditures and to select the right strategies, technologies, and processes.

While KM processes have direct effect on operational performance of manufacturing companies, managers should be aware that such performance can be significantly enhanced by developing superior process innovation capability. In today’s dynamic and competitive environment, managers should direct KM processes to support and accelerate process

367

Mediating role of product and

process

innovation levels which, in turn, will boost operational performance levels. Managers missing the crucial role of process innovation in KM-OP linkages may not be able to optimize operational performance and catch up with competitors. Furthermore, process innovation that is not easy to imitate is essential to provide a company with sustainable operational performance and competitive advantage because the knowledge related to this process innovation is unique and company specific (Koellinger, 2008; Paananen, 2009).

7.3 Limitations and future directions Although this research achieved some important implications and insights, it has some limitations that can be addressed in future research.

First, control variables such as company size, company age, industry type, process type, technology type, and age were not considered in this study. Such control variables may have affected the results. Future studies are needed to investigate the effects of control variables on the outcomes.

Second, we relied on single informants to gather data from manufacturing companies. Despite the fact that several empirical studies were based on single informants, multiple informants are expected to improve the validity of the findings and generalizability of the results. Further studies are needed with multiple informants to confirm the findings of this study.

Third, an additional limitation was related to KM construct. EFA failed to establish three constructs representing KM processes and, instead, all question items loaded onto one factor representing KM. this situation prevented us of investigating the individual contribution of KM processes on innovation types and operational performance. Additional studies are needed to investigate such individual effects.

Fourth, similar studies are needed to examine the effect of KM on innovation and performance in the service sector, particularly in developing countries where such studies are rare.

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Further reading

Nunnally, J. (1978), Psychometric Theory, 2nd ed., McGraw-Hill, New York, NY.

About the authors Ahmad Fathi Al-Sa’di earned his MBA Degree with a concentration in Management from the School of Business, University of Jordan.

Ayman Bahjat Abdallah is an Associate Professor of Operations and Supply Chain Management at the University of Jordan, School of Business, Department of Business Management. He holds a BSc in Mechanical Engineering. He received his MBA from the Ritsumeikan Asia Pacific University and his PhD in Operations Management from the Yokohama National University, Japan. He has published research papers in International Journal of Production Economics, International Journal of Business Innovation and Research, International Journal of Business and Management, International Business

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Research, Yokohama Business Review, American Journal of Operations Research, Journal of Management Research and in several international conference proceedings.

Samer Eid Dahiyat is an Associate Professor of Strategic Management and Organizational Agility at the University of Jordan, School of Business, Department of Business Management, where he earlier served as Department Chairman and Assistant Dean. He earned his PhD and MBA Degrees from the University of Huddersfield, UK, and his BSc Degree from the University of Jordan. He participated in establishing the Arab Certified Quality Manager qualification, offered by Talal Abu-Ghazaleh Group. His current research interests and publications focus on such areas as knowledge management infrastructure and processes, absorptive capacity, organizational innovation, strategic flexibility and learning and organizational agility. His earlier research was published in a number of international journals including: International Journal of Learning and Change, Benchmarking: An International Journal, International Journal of Services and Operations Management, International Journal of Commerce and Management, and Journal of Management Research. Samer Eid Dahiyat is the corresponding author and can be contacted at: [email protected]

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