Different types of innovations and dynamic capabilities in achieving timely responsiveness: Some implications for supply chain management

Abstract

Timely responsiveness (TR) enables firms to outperform their competitors by responding rapidly to market changes and seizing opportunities promptly. However, how firms can achieve TR remains poorly understood. This study investigates the ways in which firms can improve TR and draws out the implications of these in managing supply chains. Specifically, this study examines the relationships between dynamic capabilities (DCs), namely, sensing and reconfiguring capabilities, different types of innovations, and TR. The findings show that the development of product, process, and management innovations improves TR. Sensing and reconfiguring capabilities are identified as core DCs that drive the development of different types of innovations.

Keywords

dynamic capabilities product innovations process innovations management innovations timely responsiveness high-tech firms supply chains

Introduction

Time-based competition refers to an evolving business strategy that emphasizes timely responsiveness (TR) to achieve competitive advantage (Stalk, 1993; Stalk and Hout, 1990). TR, defined as the ability to respond rapidly to change, is a critical element that enables firms to outperform competitors and achieve competitiveness in a timely manner (Eisenhardt and Tabrizi, 1995; Stalk, 1993; Stalk and Hout, 1990; Williamson, 2016). As change in the business environment is pervasive and competitive pressures intensify, firms cannot just match the pace of competitors (Stalk, 1993; Vesey, 1991). Instead, they need to respond to change and changing customer needs faster than their competitors (Datar et al., 1997; Jayaram et al., 1999; Stalk, 1993). One area where adaptability and response are particularly critical is in the management of supply chains, especially when they are being disrupted (Aslam et al., 2018). Rapid response to change enables firms to adopt relevant strategies to swiftly address changes in supply patterns, such as supply shortages and disruptions, and adapt to changing customer needs by reducing lead time in their supply chain processes (Aslam et al., 2018; Handfield and Bechtel, 2002). This allows firms to seize new business opportunities to increase their market share and achieve superior performance (Datar et al., 1997; Neill et al., 2007; Pisano and Wheelwright, 1995).

Despite the significance of TR to firm competitiveness, little is known about how firms can achieve it (Neill et al., 2007; Stalk and Hout, 1990; Williamson, 2016). The purpose of this study is to empirically investigate the ways in which firms can improve TR and draw out the implications of these from a supply chain perspective. This study examines the role of product, process, and management innovations as precursors of TR. To this end, dynamic capabilities (DCs), specifically, sensing capability (SC) and reconfiguring capability (RC), are investigated as enablers of the development of different types of innovations. Additionally, instead of investigating DCs as a single multidimensional construct consisting of SC and RC, the individual influence of specific identifiable capabilities on the development of different types of innovations is investigated to clearly articulate the specific capabilities that enable or constrain the development of innovations (Eisenhardt and Martin, 2000). Joint consideration of DCs, different types of innovations, and TR helps management scholars to understand the key enablers and precursors of TR. It also offers managers insights into how they can harness the right DCs to develop innovation strategies that will enable their firms to respond to change faster than their competitors.

The remainder of the study is presented as follows. The second section presents the study’s theoretical background and the development of hypotheses regarding the relationship between different types of innovations and TR, and the association between DCs (specifically SC and RC) and different types of innovations. The third section describes the study’s methodology, including sample selection, data generated, measures, and control variables. This section also incorporates a discussion on the suitability of using partial least squares structural equation modeling (PLS-SEM) for data analysis and an evaluation of the study’s measurement models. The fourth section presents the study’s findings. The fifth section discusses the study’s contribution to multiple bodies of literature relating to time-based competition, innovation, and DCs, the implications of the study’s findings for supply chains, and the study’s limitations and recommendations for future research.

Theoretical background and hypotheses development

Different types of innovations and TR

Time-based competition stresses that TR is critical to enable firms to attain competitive advantage (Stalk, 1993; Stalk and Hout, 1990). Responding promptly to market changes and seizing opportunities allows companies to gain competitive advantage by competing on time, outdistancing competitors, and making it difficult for them to mount a response (Bowonder et al., 2010; Stalk, 1988). To achieve this, firms emphasize the strategy of time reduction across their value chain activities (Al Serhan et al., 2015; Bowonder et al., 2010; Stalk and Hout, 1990). Firms can use various approaches to reduce time spent on value chain activities (e.g., product and technology development, operations, and marketing and sales), including shortening the product development process, reducing manufacturing cycle time, and speeding new products to market (Bowonder et al., 2010; Helms and Ettkin, 2000). For firms operating within supply chains, the ability to adjust promptly to changing customer needs and seize new opportunities quickly is key to remaining competitive, especially in disrupted or rapidly changing business environments. Such firms must be able to adjust their value chain activities to develop a response advantage through responsiveness and innovations (Al Serhan et al., 2015; Bar Am et al., 2020; Gunasekaran et al., 2008; Stalk and Hout, 1990; Thomas, 1992). To do so, firms must develop DCs that will enable them to change promptly and leverage a response advantage within their supply chains (Storer and Hyland, 2011). Indeed, some researchers have argued that firms can compress the time involved in value chain activities by developing product, process, and management innovations (Pisano and Wheelwright, 1995; Song et al., 1998; Williamson, 2016).

According to the prior literature, firms use innovations as a means of adapting to change (Damanpour et al., 2009). To function effectively and maintain their competitiveness, firms need to innovate and introduce change (Damanpour et al., 2009; Gunday et al., 2011). However, scholars have advocated the need to engage in different types of innovations (Damanpour et al., 2009; Teece, 2007). Based on Teece (2007), firms must go beyond the development and use of product and process innovations and incorporate management innovations into their innovation strategies. Other scholars have argued that the development of these innovations helps firms to attain TR, as they allow firms to compress time spent on value chain activities, including product development, manufacturing systems, and product introduction (Pisano and Wheelwright, 1995; Song et al., 1998; Williamson, 2016). To accelerate the product development process and respond rapidly to change, firms need to solve new product development problems quickly, align their process technology with evolving product technology, and create supportive structures to spur collaboration between organizational members (Birkinshaw and Mol, 2006; Neill et al., 2007; Pisano and Wheelwright, 1995; Williamson, 2016). The development of product, process, and management innovations helps firms to develop resilience to cope with unexpected changes in business environments, reduce lead time in supply chain processes to launch innovative new products ahead of competitors, and respond flexibly to changes in demand, thus, underlying the firm’s competitive advantage (Lii and Kuo, 2016; Parast, 2020; Storer and Hyland, 2011; Williams et al., 2013). Overall, in line with previous literature that highlights the importance of product, process, and management innovations, this study examines the relationship between these innovations and TR. The hypotheses developed to explore these relationships are presented below.

Product innovations refer to the creation of new products or services for the marketplace (Damanpour, 1991; Li and Atuahene-Gima, 2001). The word “product” includes and equates goods produced by manufacturers with services delivered by service providers (Damanpour et al., 2009). Based on the cross-functional integration (CFI) literature, the creation of successful new products is a multidisciplinary process involving organizational members from different functions (e.g., Brettel et al., 2011; Gupta et al., 1985; Olson et al., 2001). Given that each function has a specialized role, the sharing and coordination of information and resources offered by the different functions reduces uncertainties and shortens the process of making sound decisions during the new product development process (Song et al., 1998). For instance, the joint participation of R&D and marketing departments in technological and marketing planning helps firms to quickly decide on target markets, expectations of product performance, and product launch schedules (Brettel et al., 2011; Gupta et al., 1985). Frequent interaction between functions also allows firms to integrate the diverse and specialized knowledge of their employees to obtain a broader understanding that stimulates creativity in solving product development problems rapidly (Bstieler, 2005; Song et al., 1998). Collectively, the involvement of different functions in the development of product innovations helps to compress time in the product development process (Bower and Hout, 1988). This is crucial to allowing firms, particularly those in fast-changing supply chains, to adjust to the changing needs of the supply chain and introduce new products faster than competitors (Gunasekaran et al., 2008; Lii and Kuo, 2016).

Process innovations refer to the introduction of new methods of producing products or delivering services (OECD, 2005a; Wang and Ahmed, 2004). According to Pisano and Wheelwright (1995), the development of process innovations helps firms to align their process technology with evolving product technology. Alignment between the two is critical because it ensures that firms have adequate manufacturing capabilities in place to ramp up production rapidly to introduce new products to the market promptly (Pisano and Wheelwright, 1995). Moreover, Pisano and Wheelwright (1995) observed that high-tech firms that integrated process development into the product development cycle enjoyed process improvements. This, in turn, allowed the firms to develop complex products and penetrate markets rapidly (Pisano and Wheelwright, 1995). Collectively, from a supply chain perspective, the development of process innovations helps firms to be agile and responsive to changing market conditions by reducing the lead time needed to release new products in the marketplace (Bowersox et al., 1999; Gunasekaran et al., 2008).

Management innovations refer to new management practices, processes, structures, or techniques developed by a firm (Birkinshaw et al., 2008). The development of management innovations helps firms to create supportive structures that encourage cooperation between organizational members, leading to an acceleration in the innovation process (Birkinshaw and Mol, 2006; Damanpour, 2014; Williamson, 2016). In his qualitative study, Williamson (2016) observed that Chinese firms that adopted a structure that combined a vertical hierarchy and horizontal flexibility managed to accelerate the innovation process and transform rapidly. This is because the combined structure (1) supported flexibility in recombining resources and solving problems through the participation of heads or experts across relevant departments under horizontal flexibility, and (2) was complemented by a vertical hierarchy that imposed intense pressures on participating parties to diagnose rapidly and find solutions to problems (Williamson, 2016). This combination of horizontal flexibility and vertical hierarchy produced faster responses to change among these firms, compared to their competitors (Williamson, 2016). Further, management innovations can improve customer and supply chain responsiveness by establishing structures that are conducive to and that support new ways of working (Birkinshaw and Mol, 2006; Williamson, 2016). According to Birkinshaw and Mol (2006), the introduction of business-cell structures enabled Litton Interconnection Products, a firm that assembles back-plane systems for computers in Scotland, to pursue new ways of working that improved customer responsiveness by enabling each cell of multi-skilled employees to fulfill a single customer’s entire needs. Collectively, the development of management innovations is essential for firms in supply chains, as it enables them to accelerate the innovation process, increase customer responsiveness, and match changing environmental requirements by having supportive structures and innovative management routines in place (Williams et al., 2013).

Considering the discussion above, the development of product, process, and management innovations allows firms to reduce the time they spend on value chain activities in various ways. This includes addressing product development problems swiftly, ramping up production quickly, and accelerating the innovation process (Bstieler, 2005; Pisano and Wheelwright, 1995; Song et al., 1998; Williamson, 2016). The resulting reduction in time associated with these activities provides firms with a response advantage to change and accelerate their supply chain processes (Jayaram et al., 1999; Lin et al., 2012; Stalk, 1993; Thomas, 1992). The greater the reduction of time gained through the development of different types of innovations, the better firms will be at using a response advantage to respond to change and supply chain changes than their competitors (Stalk, 1993; Thomas, 1992). Accordingly, it is inferred that

H1: The more a firm develops (a) product innovations, (b) process innovations, and (c) management innovations, the greater the level of TR it achieves.

DCs and different types of innovations

DCs emphasize the ability of firms to sense changes in the business environment and reconfigure their resources to adapt to them (Teece, 2007; Teece et al., 1997). Resources are defined broadly to include both tangible and intangible assets (Helfat et al., 2007). The ability of firms to adapt to environmental changes using DCs underlies their achievement of competitive advantage (Teece, 2007; Teece et al., 1997). According to Eisenhardt and Martin (2000), DCs are identifiable and involve specific routines. Scholars have consistently identified that SC and RC are integral parts of DCs (e.g., Teece, 2007; Wang et al., 2015). SC emphasizes the ability of firms to learn and keep abreast of evolving business trends, as well as generate input into the development of innovations (Teece, 2007; Wang et al., 2015). RC stresses a firm’s ability to renew resources through various means, including redefining strategies, recombining existing and new technologies, and aligning resources with environmental requirements to create innovations (Pandza and Holt, 2007; Teece, 2007).

The notion of DCs has been associated with innovation in prior research (Teece, 2007; Verona and Ravasi, 2003). According to Teece (2007), once firms sense new technological/market opportunities, it is imperative for them to seize opportunities through innovations. Based on past studies, firms need to keep abreast of events in the business environment and realign their resources to develop innovations and respond promptly to change (Pisano and Wheelwright, 1995; Teece, 2007). As such, the employment of DCs, especially SC and RC, helps firms to develop different types of innovations (Teece, 2007). The use of SC allows firms to explore the business environment and generate market insights that will help them to develop different types of innovations (Harreld et al., 2007; Teece, 2007). The employment of RC enables firms to develop different types of innovations by helping them to renew their resources to match changing market conditions (Sirmon and Hitt, 2003; Teece, 2007). While firms control resources such as assets, capabilities, and organizational processes (Barney, 1991), the mere possession of such heterogeneous resources is unlikely to allow firms to outperform their competitors unless they can reconfigure and align their resources to match environmental requirements (Blome et al., 2013). For example, firms can modify their operational routines and create new technological capabilities to develop innovations to seize new business opportunities (Lavie, 2006).

The important role of SC and RC in the development of innovations is undeniable. By actively learning from customers, suppliers, and competitors, firms can observe overall market conditions, be alert to opportunities and challenges within their supply chains, and proactively anticipate and respond flexibly to change using appropriate innovations and adaptations (El Baz and Ruel, 2021; Williams et al., 2013). The use of RC allows firms to integrate and reconfigure internal and external competencies in dynamic business environments (Teece et al., 1997). As such, RC is key to the creation of new supply chain configurations as it emphasizes adjustments to changing circumstances and opportunities (Wei and Wang, 2010). In other words, RC helps firms to be more responsive in adapting to environmental requirements, thus mitigating the vulnerabilities of firms to unexpected changes (such as the supply disruptions that occurred during the COVID-19 pandemic, for example) (El Baz and Ruel, 2021).

Overall, given that (1) sensing the business environment and reconfiguring organizational resources are integral parts of DCs, and (2) the argument that such capabilities are helpful in the development of different types of innovations in fast-changing environments such as those being experienced by supply chains during the COVID-19 pandemic, this study investigates the role of SC and RC in the development of different types of innovations (Harreld et al., 2007; Sirmon and Hitt, 2003; Teece, 2007; Wang et al., 2015).

SC and different types of innovations

A firm’s business environment is constantly evolving due to changes in customer needs, technology, the actions of competitors, and global trade rules and regulations (Teece, 2007). To remain current, firms in supply chains must harness SC (i.e., the ability to scan for, learn about, and generate market insights from the business environment) to develop innovations (Teece, 2007). As information and knowledge generated by external sources influence all innovation activities, this study proposes that SC helps firms to develop various types of innovations (Cohen and Levinthal, 1990; Yam et al., 2011).

The use of SC may facilitate the development of product innovations by enabling firms to sense across markets and competitive environments, thereby increasing their market knowledge and their understanding of the expressed and latent needs of their customers, as well as the product ranges, strategies, and future directions of their competitors (Morgan et al., 2009; Sandvik and Sandvik, 2003). This know-what advantage can help firms recognize market changes and develop differentiated products that are compatible with customer needs (Morgan et al., 2009; Slater and Narver, 1995). For example, Harreld et al. (2007) observed that IBM’s development of outsourcing solutions was attributable to its ability to generate market insights and recognize that the company’s customers preferred to purchase solutions to problems rather than purchase IT infrastructure. To develop new innovative products that create value for their customers, Apple learned from customer comments to anticipate the great features that its products could offer to customers, as well as improve their products by incorporating new features to satisfy new customer requirements (Safian, 2018). Learning from its failure of developing Windows 8 from an inside-out approach involving engineers working in a vacuum, Microsoft’s successful Windows 10 was developed by focusing on a customer-centric approach that prioritized customer and market feedback and defined target customers (Korst and Whitler, 2020). Furthermore, as the launch and timely delivery of new products to customers hinges on the availability of raw materials/inputs provided by suppliers, firms need to harness SC to internalize knowledge such as supplier inventory levels, lead time, and delivery dates (Lii and Kuo, 2016; Williams et al., 2013). By acquiring such knowledge, firms can determine how to use quality materials/parts to deliver differentiated new products to meet the requirements of their customers (Lii and Kuo, 2016).

Furthermore, the use of SC may help firms to develop process innovations. From past research, access to external knowledge sources is important as information and knowledge related to improving production/delivery methods and aligning process technology with evolving product technology is often available in external business environments (Cohen and Levinthal, 1990; Piening and Salge, 2015). By using SC, firms can scan across markets and technologies and learn from their customers and suppliers (Teece, 2007). Specifically, acquiring and absorbing new technological knowledge from suppliers improves the awareness of firms to changes in manufacturing technology that can help them to minimize potential manufacturing risks and improve their manufacturing processes (Lii and Kuo, 2016). The awareness of technological changes also helps firms to identify opportunities to renew their resources and generate novel ideas and process development concepts (Piening and Salge, 2015; Zheng and Wang, 2020).

Additionally, the use of SC may lead to the development of management innovations. According to Birkinshaw and Mol (2006), changes in the business environment that are perceived as strategic threats by managers (competitors’ moves, price wars, etc.) often motivate firms to respond with management innovations. Frynas et al. (2018) observed that the desire to address competitive pressures is one of the factors that motivated China’s Haier Group, a white goods company, to introduce Rendanheyi, a platform of managerial practices that transformed the company from a traditional hierarchical manufacturer into a responsive, online-based entrepreneurial firm. Given that firms in supply chains are motivated by environmental influences to innovate, their use of SC allows them to learn about competitors and the general business environment (e.g., technological developments, economic trends, and government regulations) and respond to changing market conditions by offering appropriate management responses and innovations (Modgil and Sharma, 2017; Schoemaker et al., 2018; Teece, 2007).

Instead of limiting scanning to internal organizational memory, SC allows firms to collectively access and learn from the diversified and complementary knowledge of a variety of external sources, such as competitors, customers, and suppliers (Ebersberger and Herstad, 2011; Teece, 2007). Information and knowledge obtained from the broader business environment helps firms to generate market insights, keep abreast of environmental changes, and be aware of the potential to innovate (Harreld et al., 2007; Teece, 2007). The more input firms obtain through SC, the more alert they become of environmental changes and the need to address these changes through the development of different types of innovations (Teece, 2007). Given the above discussion, it is inferred that

H2: The more a firm uses SC, the greater the level of the development of (a) product innovations, (b) process innovations, and (c) management innovations.

RC and different types of innovations

As markets and technologies change, a firm’s resources can easily become obsolete (Teece, 2007). It is therefore important for firms to emphasize their use of RC, namely, the ability to reconfigure and align their resources with changing market conditions (Teece, 2007). Specifically, reconfiguring resources and aligning them with environmental requirements helps firms to develop innovations and maintain evolutionary fitness (Teece, 2007; Williamson, 2016). Firms can reconfigure resources through various means, such as by redefining strategies and creating resources (Grant, 1996; Teece, 2007). For instance, in response to the impacts of the COVID-19 pandemic, Starbucks redefined and accelerated its earlier plans of adding more drive-through locations from the next 3–5 years to the next 18 months (Kelso, 2020). To diversify business during the COVID-19 pandemic, the AirAsia Group accelerated the development and launch of an all-in-one digital platform (super app) that encompasses not just travel and entertainment but also shopping, food delivery, and cashless payments (Dzulkifly, 2020).

To develop product innovations, firms in supply chains can harness RC to extend their core capabilities, namely, the set of knowledge that differentiates a firm from its competitors and underlies its competitive advantage (Grant, 1996). As business environments change, new technologies can make core capabilities irrelevant, turning them into core rigidities (Leonard-Barton, 1992; Meyer and Utterback, 1993). To avoid this, firms must regularly extend their core capabilities by incorporating new types of knowledge (Grant, 1996). A planned renewal of core capabilities can help firms address the challenge of technological obsolescence and close the gap in product innovations between required capabilities and the requirements of the business environment (Meyer and Utterback, 1993). Therefore, improvements in core capabilities help firms to align their resources to develop new products to satisfy changing market conditions (Galunic and Rodan, 1998; Teece, 2007).

Furthermore, scholars have proposed that reconfiguring knowledge is not just important to developing new products, it is also imperative to developing process and management innovations (Birkinshaw and Mol, 2006; Grant, 1996; Henderson and Clark, 1990; Kogut and Zander, 1992). While the specialized knowledge of employees is an essential element in the development of all types of innovations (Damanpour and Schneider, 2006; Grant, 1996), the mere possession of a workforce with specialized knowledge does not provide firms with a source of competitive advantage (Grant, 1996). The knowledge-based view (KBV) of the firm recognizes that the integration and recombination of knowledge are the key drivers of organizational innovations and competitive advantage (Galunic and Rodan, 1998; Grant, 1996). As a result, firms must reconfigure the specialized knowledge of their employees (Grant, 1996). Firms can, for instance, integrate the specialized knowledge of these individuals in new ways using team structures (Alavi and Tiwana, 2002). By synthesizing the broader knowledge and expertise of employees, firms can easily identify new opportunities and threats and incorporate important new ideas in the development of different types of innovations (Alavi and Tiwana, 2002; Santos et al., 2004). Firms can use the complementary specialized knowledge of individual employees and the richness of information shared amongst them to generate creative ideas and a heterogeneous knowledge base to develop different types of innovations (Alavi and Tiwana, 2002; Davis and Aggarwal, 2020). More importantly, the reconfiguration of employees’ specialized knowledge allows firms to harness fresh, integrated perspectives and situation-specific knowledge to develop new or enhanced heuristics to solve new problems and develop innovations that match environmental requirements (Alavi and Tiwana, 2002; Grant, 1996; Teece, 2007). This approach prevents intellectual lock-in at firms and enhances the potential of firms to adapt to environmental changes and seize new business opportunities through the development of different types of innovations (Kaplan and Vakili, 2015; Teece, 2007). Based on the above discussion, it is inferred that

H3: The more a firm uses RC, the greater the level of the development of (a) product innovations, (b) process innovations, and (c) management innovations.

Methodology

Data collection

A survey was used to collect data from high-tech firms in Malaysia, as the country’s high-tech industry provides a relevant research context for this study. To compete with foreign competitors in the domestic market and sustain their competitiveness in foreign markets, Malaysian high-tech firms actively engage in R&D, are driven by change and innovation, and offer products/services that meet international standards (Malaysia External Trade Development Corporation, 2017; Performance Management and Delivery Unit, 2010). Given the dynamic nature of their business environments, it would be essential for Malaysian high-tech firms to deploy SC and RC in the development of innovations and respond promptly to change (Stalk, 1993; Teece, 2007). The Orbis database provided the sampling frame for this study as it offers a representative number of high-tech firms (n = 1,112) in Malaysia (BUREAU VAN DIJK, 2018). After initial telephone calls were made to invite high-tech firms from the target population to participate in this study, an electronic questionnaire was emailed to 443 firms that expressed an interest in participating. A total of 138 completed questionnaires was received, providing a response rate of 12.4%. The study’s sample size is considered normal for strategy research: Wang et al.’s (2015) research on success traps and DCs used 113 samples, while Atalay et al.’s (2013) study of types of innovations and firm performance used 113 samples. In terms of the characteristics of the firms that participated in this study, 60.9% were high-tech manufacturers, while 39.1% were high-tech service providers. The former was involved in manufacturing computers, and electronic, optical, and electrical products, while the latter included software developers and telecommunications service providers. Study respondents included senior managers (e.g., Strategic Planning Directors, Project Directors, and Sales Directors), as well as executive-level officers (e.g., Chief Executive Officers, and Chief Operations Officers).

A proxy of early and late respondents was used to check for the possibility of non-response bias (Armstrong and Overton, 1977). The Mann–Whitney U test results for the key constructs show no statistical differences (p > 0.05) between the two groups. Thus, non-response bias is not a concern in this study. An ex-ante approach was used from the beginning of the study to mitigate concerns about common method bias (Bao et al., 2012). A questionnaire was developed with relevant and clear scales using different response formats, and respondent anonymity was ensured (Podsakoff et al., 2003). An ex-post examination was conducted at the end of the study using Harman’s single factor test (Podsakoff and Organ, 1986). The results show that the first factor explains only 38.9% of the total variance. Since the first factor accounts for less than 50% of the total variance, the study’s dataset is unlikely to be threatened by common method bias (Fuller et al., 2016; Podsakoff and Organ, 1986).

Measurement

A scale measuring TR was based on that of Neill et al. (2007). The scale focused on the ability of a firm to respond rapidly to change or competitive pressures in various aspects, such as by changing the firm’s strategy, developing and implementing strategies, and meeting changes in customers’ product/service needs.

Based on previous literature (i.e., Das and Joshi, 2007; Subramanian and Nilakanta, 1996), a scale was used to measure process innovation. The scale captured the extent to which a firm develops new technologies, methods, and procedures to produce products or services. A management innovation scale, adapted from Wang and Ahmed (2004), was used to measure the development and improvement of new management approaches and new ways to control and direct departments.

A scale adapted from Neill et al. (2007) was used to evaluate SC. The scale measured a firm’s ability to learn about market conditions, to convert the understanding of such conditions into business opportunities, and to influence organizational objectives. A scale based on previous studies (i.e., Ambrosini et al., 2009; Kodama, 1992; Morgan et al., 2009; Sirmon and Hitt, 2003) was used to measure RC. The scale measured the alignment of resources with changing market conditions through several resource renewal approaches, such as redefining marketing strategies and rearranging existing resources in new ways.

A seven-point Likert scale, with values ranging from 1 (strongly disagree) to 7 (strongly agree), was used to elicit and measure data regarding SC and RC, process and management innovations, and TR. Product innovation was assessed using a scale adapted from that of Li and Calantone (1998). Using a seven-point Likert scale, with values ranging from 1 (much worse) to 7 (much better), respondents were asked to evaluate the extent to which new products (e.g., computers, software, and network solutions) created by their firm compared to those created by their top three major competitors in the past 3 years, in aspects such as newness, compatibility, uniqueness, and functionality. The measurement scales used in this study are presented in Appendix.

Several control variables were included in this study. Sector type was controlled for because differences in business nature, customer base, and competitors between sectors might lead high-tech manufacturers and service providers to use their SC and RC differently in the development of different types of innovations, influencing the outcome of TR (Voss and Voss, 2013; Zhang et al., 2017). Respondents chose the sector type of their firm from categories that included manufacturers of computer, electronic, optical, and electrical products, software developers, and telecommunications service providers. Second, firm status was controlled for, given that multinational companies could use their better access to foreign knowledge and technology to broadly sense and reconfigure the specialized knowledge of their employees across global subsidiaries to develop different types of innovations and respond to change faster than their domestic counterparts (Carboni and Russu, 2018). Respondents were asked to choose either “domestic company” or “multinational company” to indicate the status of their firms. Third, firm size was controlled for, given that larger firms have greater resources and can deploy a broader range of skills to develop different types of innovations to respond to change faster than smaller firms (Peeters et al., 2014; Subramanian and Nilakanta, 1996). Respondents chose the size of their firms from the following categories: (1) less than 10 employees; (2) 10–49 employees; (3) 50–249 employees; and (4) 250 employees and above (OECD, 2005b).

Data analysis

The objective of this study is to examine the ways in which firms can improve TR. It is proposed that the attainment of TR hinges on the development of product, process, and management innovations, which rely on the use of SC and RC. Based on the best knowledge available, no prior empirical studies have been undertaken to date to investigate the relationships between these constructs. As such, this study involves the analysis of a predictive research model that is still not well established (Hair et al., 2014). The use of PLS-SEM is appropriate for this study as it is commonly used for analyzing nascent predictive research models (Hair et al., 2014). Furthermore, this study’s sample size is small, and its complex research model consists of six latent variables, thus calling for the use of PLS-SEM. PLS-SEM can estimate complex models without identification issues despite the use of small sample sizes (Anderson and Swaminathan, 2011; Hair et al., 2014).

Construct validity and reliability

The results of the convergent validity and internal consistency reliability tests of the reflective first-order constructs are presented in Appendix. Convergent validity is established on the basis that the loadings of all indicators are statistically significant at p < 0.001, that most loadings are greater than the threshold of 0.708, and that the average variance extracted (AVE) values are above the cut-off of 0.50 (Hair et al., 2014). As the composite reliability values for all reflective first-order constructs exceed the threshold of 0.70, the establishment of internal consistency reliability is confirmed (Hair et al., 2014). As shown in Table 1, discriminant validity is also established, as the results from an analysis using Fornell and Larcker’s criterion show that the square root of each construct’s AVE is greater than its correlations with other constructs (Hair et al., 2014).

Table 1.

Descriptive statistics and results of Fornell-Larcker criterion.

		Mean	SD	1	2	3	4	5	6
1	Sensing capability	5.40	0.748	0.733
2	Reconfiguring capability	5.47	0.897	0.449*	0.844
3	Product innovation	4.92	0.826	0.336*	0.484*	0.837
4	Process innovation	5.22	0.913	0.447*	0.593*	0.507*	0.849
5	Management innovation	5.30	0.874	0.318*	0.586*	0.379*	0.675*	0.809
6	Timely responsiveness	4.97	0.924	0.463*	0.672*	0.442*	0.553*	0.533*	0.857

Note: *Correlation is significant at the 0.01 level (2-tailed). The diagonal elements (in bold) represent the square root of the AVE; non-diagonal elements represent correlation between constructs. SD, standard deviation.

Results

Table 1 presents the descriptive statistics for all constructs. The results of a collinearity assessment indicate that two sets of predictor constructs (i.e., SC and RC and types of innovations) are unlikely to be influenced by a collinearity problem, as their VIF values (which range between 1.266 and 2.245) are below the threshold of 5 (Hair et al., 2014). Hence, the structural model can be considered. The PLS algorithm was adopted with a path weighting scheme and 300 maximum iterations to estimate the structural model (Hair et al., 2014). To determine the significance of path coefficients, a bootstrapping procedure was run using 5000 bootstrap subsamples and 138 bootstrap cases (Hair et al., 2014).

Figure 1 presents the findings of the study’s structural model. Product (β = 0.207; p < 0.01), process (β = 0.260; p < 0.01), and management (β = 0.302; p < 0.01) innovations are statistically significant and positively associated with TR. Accordingly, Hypotheses 1a, 1b, and 1c are supported. The results show that SC has a significant positive influence on product (β = 0.181; p < 0.05) and process (β = 0.258; p < 0.001) innovations, supporting Hypotheses 2a and 2b. However, the path coefficient (β = 0.097) for SC is not significantly related to management innovations. As a result, Hypothesis 2c is not supported. The findings also show that RC has a significant positive impact on product (β = 0.405; p < 0.001), process (β = 0.486; p < 0.001), and management (β = 0.563; p < 0.001) innovations. As such, they support Hypotheses 3a, 3b, and 3c. Based on Hair et al. (2017), the structural model in PLS-SEM is fundamentally assessed using its predictive accuracy and relevance. The study’s structural model exhibits predictive accuracy as the coefficient of determination (R²) values of the endogenous constructs are moderate to substantial, ranging between 0.263 and 0.417 (Berghman et al., 2013; Vock et al., 2013). The model also shows predictive relevance, as the blindfolding procedure run using 9 as the omission distance generates above zero values for the Stone-Geisser’s Q² coefficients for endogenous constructs (Hair et al., 2014).

Figure 1.

Research findings.

To check whether control variables have any effects on hypothesized causal paths, a PLS multi-group analysis (PLS-MGA) was run following the guidelines of SmartPLS (2015). The dataset was first split into two groups of data (e.g., domestic and multinational companies) (SmartPLS, 2015). Subsequently, the structural models of each of the control variables (e.g., firm status) were estimated separately for the two groups of data (SmartPLS, 2015). A PLS-MGA was then performed to determine if the differences in the group-specific PLS path model estimations for each of the control variables were significant (SmartPLS, 2015). As summarized in Table 2, the results show that none of the control variables is likely to influence any of the hypothesized causal paths, as the p-values for group-specific path coefficient differences for the control variables (sector type, firm size, and firm status) are neither less than nor equal to 0.05, nor at least 0.95 (SmartPLS, 2015), thus increasing the credence of the study’s findings.

Table 2.

PLS-MGA results.

Sector type
	High-tech manufacturers (n = 84) path coefficient	High-tech service providers (n = 54) path coefficient	Path coefficient difference	P-value
SC → PDI	0.173	0.207	−0.034	0.853
SC → PCI	0.305	0.200	0.105	0.460
SC → MI	0.053	0.119	−0.066	0.681
RC → PDI	0.375	0.480	−0.105	0.480
RC → PCI	0.455	0.537	−0.082	0.641
RC → MI	0.613	0.496	0.117	0.557
PDI → TR	0.184	0.236	−0.052	0.717
PCI → TR	0.283	0.246	0.037	0.856
MI → TR	0.273	0.362	−0.089	0.657
Firm status
	Domestic companies (n = 34) path coefficient	Multinational companies (n = 104) path coefficient	Path coefficient difference	P-value
SC → PDI	0.196	0.175	0.021	0.829
SC → PCI	0.203	0.264	−0.061	0.856
SC → MI	0.306	0.066	0.240	0.166
RC → PDI	0.419	0.409	0.010	0.924
RC → PCI	0.573	0.469	0.104	0.505
RC → MI	0.610	0.573	0.037	0.860
PDI → TR	0.257	0.196	0.061	0.698
PCI → TR	0.105	0.304	−0.199	0.448
MI → TR	0.432	0.277	0.155	0.513
Firm size
	Small and medium-sized enterprises (n = 102) path coefficient	Large firms (n = 36) path coefficient	Path coefficient difference	P-value
SC → PDI	0.175	0.192	−0.017	0.895
SC → PCI	0.330	0.046	0.284	0.133
SC → MI	0.181	−0.201	0.382	0.070
RC → PDI	0.409	0.447	−0.038	0.809
RC → PCI	0.454	0.597	−0.143	0.472
RC → MI	0.519	0.772	−0.253	0.283
PDI → TR	0.257	0.125	0.132	0.486
PCI → TR	0.252	0.248	0.004	0.938
MI → TR	0.286	0.365	−0.079	0.727

Note: SC, sensing capability; RC, reconfiguring capability; PDI, product innovation; PCI, process innovation; MI, management innovation; TR, timely responsiveness.

Discussion and conclusion

Contribution of the study

This study contributes to multiple bodies of literature in several significant ways. First, the research contributes to the literature on time-based competition. Unlike past studies that focused on the association between time-based programs and time-based performance, and the relationship between time-based performance and firm performance (e.g., Handfield, 1993; Jayaram et al., 1999; Sim and Curatola, 1999; Vickery et al., 1995), this study examines the ways in which firms can improve TR. Specifically, the findings show that the development of product, process, and management innovations improves TR, that the use of RC enables the development of these three innovation types, and that the use of SC enables the development of only product and process innovations. Drawing from the time-based competition literature, this study’s findings highlight the importance of the development of product, process, and management innovations in helping firms to shorten the time they spend on value chain activities (such as product development and manufacturing) and improve their ability to respond rapidly to environmental changes (Al Serhan et al., 2015; Stalk and Hout, 1990). Additionally, the findings of this study show that SC and RC are the key enablers of the development of different types of innovations. The findings confirm the importance of developing and using RC, as this enables firms to renew and align their resources with the requirements of environmental changes. Doing so enables firms to develop both technological (product and process) and non-technological (management) innovations. Specifically, firms that can integrate and recombine the specialized knowledge of their employees can leverage complementary diverse perspectives and situation-specific knowledge to create novel approaches to problem-solving and build different types of innovations that suit changing market conditions (Alavi and Tiwana, 2002; Grant, 1996; Teece, 2007). In contrast, the results of this study indicate that the use of SC enables the development of technological innovations only. This narrower outcome is likely because the sources for improving production/delivery methods and developing rapidly evolving product and process technologies come primarily from external business environments (Cohen and Levinthal, 1990; Piening and Salge, 2015; Pisano and Wheelwright, 1995). SC plays a major role in aligning a firm’s process and product technologies to create differentiated products to meet changing customer needs that are identified from systematic scans across markets and technologies, learning from customers and suppliers, and the broadening of market and technological knowledge (Harreld et al., 2007; Piening and Salge, 2015; Pisano and Wheelwright, 1995; Wilden and Gudergan, 2015). Despite its importance in scanning and sensing the external environment, the use of SC does not help firms to develop management innovations, which require the congealing of internal and external insights and capabilities and involve a confluence of complex social systems (Birkinshaw and Mol, 2006). The development of management innovations appears to be based on acute necessity, such as when managers perceive external pressures as critical strategic threats that need to be addressed with radically new management practices, strategies, or structures (Birkinshaw and Mol, 2006; Frynas et al., 2018). Overall, the present research advances the literature by drawing a more rounded picture of how DCs influence the development of different types of innovations which, in turn, helps firms to improve TR.

Second, this research contributes to the DC literature by highlighting that firms need to develop management innovations, in addition to product and process innovations, to respond to change promptly. Although DCs allow firms to renew resources and adapt to change through the development of innovations (Teece, 2007), there is scant knowledge of how they influence the development of different types of innovations. Given the existence of many types of DCs and that different types of innovations fundamentally have different attributes, the drivers used to develop them would vary (Pavlou and El Sawy, 2011; Subramanian and Nilakanta, 1996). By examining the individual influence of specific identifiable capabilities (i.e., SC and RC) on the development of different types of innovations together, this study offers a nuanced understanding that SC and RC have a heterogeneous impact on the development of product, process, and management innovations. While Verona and Ravasi (2003) showed that creating, absorbing, integrating, and reconfiguring knowledge drives the development of product innovations, this study extends that research by demonstrating that reconfiguring resources beyond just knowledge enables product innovations and allows firms to develop process and management innovations. This study, however, finds SC to be a narrower contributor that plays a significant role in enabling technological innovations. This shows that the two DCs (RC and SC) play different and complementary roles. The findings underscore the fact that understanding nuanced heterogeneous impacts is crucial to harnessing the right DCs to effectively create different types of innovations (Girod and Whittington, 2017).

Third, this research contributes to the innovation literature, especially studies focusing on different types of innovations, in two aspects. Unlike several previous studies that focused on information sourcing practices and different sources of external knowledge (e.g., suppliers, customers, and competitors) as predictors of the development of different types of innovations (Antonelli and Fassio, 2016; Varis and Littunen, 2010), this study focuses on the role of DCs. Specifically, the research identifies that the use of SC and RC drives the development of different types of innovations. Based on the findings of this study, RC is essential for the development of product, process, and management innovations, whereas SC is effective in the generation of product and process innovations only. Furthermore, in contrast to past studies that focused on the impact of different types of innovations on firm performance (such as perceived relative performance to competitors and firm growth), and on different aspects of firm performance (i.e., innovative, production, market, and financial performance) (Atalay et al., 2013; Gunday et al., 2011; Varis and Littunen, 2010), this study adds to the literature by substantiating that the development of product, process, and management innovations enables firms to improve TR.

Supply chain implications

In the current highly connected world, agile adaptation and TR are critical strategies that firms need to employ to effectively coordinate their supply chain activities, from sourcing raw material to the delivery of products to customers (Lii and Kuo, 2016; Mo Yang et al., 2007). Indeed, marketplace volatility has elevated the platform of competition from the firm level to the supply chain level (Aslam et al., 2018; Reefke et al., 2014). This has made firms increasingly aware of the need to emphasize time-based competition and TR to create competitive advantage in their supply chains (Stalk and Hout, 1990; Teece et al., 1997; Williamson, 2016).

According to the study’s findings, firms in supply chains need to focus on the development of product, process, and management innovations to improve TR since the development of these types of innovations can help reduce waste and time spent on value creation activities within their supply chains. This allows firms to speed up the lead time of their supply chain processes and gain a response advantage that allows them to seize new opportunities ahead of their competitors (Al Serhan et al., 2015; Stalk and Hout, 1990; Williamson, 2016). The agility that firms acquire from the development of different types of innovations helps them to cope with dynamic business environments or unexpected changes (e.g., the impacts of the COVID-19 pandemic) that underlie supply chain competitiveness (Lii and Kuo, 2016; Parast, 2020; Williamson, 2016).

To facilitate the development of different types of innovations, firms in supply chains must nurture and use DCs, specifically SC and RC. RC is a catalyst in the development of product, process, and management innovations through the renewal of resources. In particular, the agility and adaptability provided by an RC are crucial for firms to develop product innovations. By using an RC, firms can extend their core capabilities to match their resources to environmental requirements to develop new products (Grant, 1996; Teece, 2007). Furthermore, the use of RC allows firms to reconfigure their internal and external competencies and integrate and collaborate with geographically diverse suppliers (Teece et al., 1997; Zanni, 2020). Diversification of a firm’s suppliers is critical to preventing supply shortages and disruptions, such as those caused by lockdowns and shutdown of suppliers’ factories associated with the COVID-19 pandemic (Zanni, 2020). Dual/multiple sourcing of critical parts/components can help firms to deal effectively with unexpected changes in their supply chains, allowing them to continue production using high-quality parts and components from suppliers, as well as provide new or innovative products to customers on a timely basis (Lii and Kuo, 2016; Zanni, 2020). The COVID-19 pandemic has brought to surface the vulnerabilities of global supply chains and the need to rapidly adapt and adjust to them (The Economist Intelligence Unit, 2020). The use of RC is critical to helping firms to build the resilience they need to cope with unexpected changes in their supply chains, as it allows them to reconfigure their supply chains to ensure ongoing production and the timely introduction of new products (Teece, 2007; Wei and Wang, 2010).

Additionally, the use of RC can help firms to develop process innovations. Firms can use RC to create new capabilities, such as big data analytics capabilities (or digital capabilities), by integrating different types of resources. In the case of big data analytics capabilities, resources might include items such as big data, analytics, organizational information systems, and the acquisition of new capabilities (e.g., hiring personnel such as data analysts with relevant skillsets) (Lavie, 2006; Modgil and Sharma, 2017; Sanders, 2016). The ability to generate real-time data, extract meaningful insights, and develop these into intelligence from extensive heterogeneous digitized datasets can help firms to create superior value propositions for new processes to improve their supply chains (Aslam et al., 2018; Zhong et al., 2016). For instance, PepsiCo invested in technology and hired 200 full-time e-commerce employees to automate the company’s supply chain and networks of distributors and retailers (Digital Commerce 360, 2018). This action enabled managers to assign specific tasks on a timely basis and helped distributors and retailers track inventory and automatically generate the perfect order based on inventories, counter trends, and sales histories (Digital Commerce 360, 2018). Collectively, the use of RC to automate its supply chain helped PepsiCo to improve its supply chain processes and allow customers to find the right products at the right time (Digital Commerce 360, 2018). Additionally, firms that leverage RC to develop big data analytics capabilities can perform “what-if” analyses and uncover patterns and associations to identify causes and effects related to manufacturing processes (Sartorius, 2019). Using a systematic, analytical approach can help firms to diagnose and identify defects in processes and pinpoint problems that lead to downtime, allowing them to harness effective solutions to reduce waste, improve product quality, and enhance manufacturing processes (Sartorius, 2019).

Furthermore, the use of RC can help firms to develop management innovations. To ensure that collaboration efforts within the supply chain succeed, firms must instill and nurture teamwork and cooperation among employees in the first place (Alavi and Tiwana, 2002; Brown, 2019). To do so, firms must create structures to allow knowledge sharing and collaboration among employees (Birkinshaw et al., 2008; Brown, 2019). By using RC, firms can introduce new management practices by setting aside time for employees to share and create knowledge, by aligning employee performance evaluations and goal setting with collaboration efforts, and by enabling integration among departments (Brown, 2019).

In addition to the use of RC, firms in supply chains should use SC to harness market insights generated from external sources (e.g., competitors, customers, suppliers, and the broader business environment) to guide them in the development of product and process innovations that are aligned with changing market conditions (Ebersberger and Herstad, 2011; Teece, 2007). The use of the SC is imperative in the development of product innovations as it enables firms to scan and identify suppliers that match their specific requirements for desired quality inputs within a scheduled timeframe (Galankashi et al., 2016). Firms that can source quality supplies promptly can supply customers with new products rapidly. Furthermore, the use of SC allows firms to apply market insights gained from external sources to develop new products, and to adopt relevant marketing strategies to promote them. For example, Samsung Electronics achieved superiority in its supply chain by sensing and understanding customer preferences and incorporating them into product design (Supply Chain 247, 2013). In addition to sensing and comparing marketing strategies with those of its competitors (e.g., Apple), Samsung used social monitoring tools to improve its marketing strategies by monitoring the sentiments displayed by potential customers on different social media platforms, gathering actionable insights, and discovering trends from the information collected (Stradley, 2019).

Furthermore, the use of SC paves the way for firms to develop process innovations. This is because SC allows firms to be aware of and identify opportunities to renew their resources to improve/develop manufacturing processes based on technological knowledge absorbed from suppliers (Lii and Kuo, 2016; Teece, 2007). The employment of SC also helps firms to learn about and benchmark other companies, as well as keep abreast of technological changes that can help them to solve business problems through the introduction of process innovations (Liker, 2020; Piening and Salge, 2015). For instance, Denso, a world-renowned automotive parts supplier (Toyota’s largest supplier), explored technological changes and learned about and benchmarked other companies to solve the challenges it faced (Liker, 2020). The result was the development of a factory IoT platform that links 130 production factories globally (DENSO, 2020). Leveraging information technology and the Internet of things (IoT), the factory IoT platform allowed factories to respond promptly to production changes based on local demand, thus helping Denso to strengthen its global production system (DENSO, 2020). Overall, firms need to use both SC and RC to develop an appropriate set of innovations to improve speed to create a timely response advantage in their supply chains.

Limitations and future research

As with most research, this study is subject to limitations. First, the study is focused on a single industry, the high-tech industry. While the study of a single industry helps to generate context-specific research findings and provides a better understanding of a phenomenon related to that context (Molina-Azorin, 2016), it may potentially limit the generalizability of the findings to other industries. Future research could involve firms in other industries, such as those in the fast-moving consumer goods (FMCG) industry, to evaluate the generalizability of the present research findings. Second, this study’s sample came from the high-tech industry in Malaysia. Given that the findings reflect the context of an upper-middle-income nation, they may not be generalizable to the context of other nations, particularly high-income nations. Future research could evaluate the generalizability of this study’s findings to nations of different economic contexts. Third, the association between the use of SC and the creation of management innovations remains to be explored, as this study’s findings do not support this association. The findings suggest that the association might not be explained by focusing only on SC. Given that managers’ perceptions of environmental changes may influence whether their firms need to develop management innovations to deal with strategic threats, future research should investigate whether the influence of the use of SC on the development of management innovations is contingent on managers’ perceptions of environmental changes (Birkinshaw and Mol, 2006; Frynas et al., 2018). This may help to offer a more nuanced understanding of the relationship between SC and the development of management innovations, particularly how managers’ perceptions of environmental changes influence this relationship. Fourth, this study identifies specific DCs that can be used to develop different types of innovations, and it shows the influence of these innovations on TR. Future research should explore extending this study’s findings through qualitative research to develop a deeper understanding of the idiosyncratic processes involved in how the development of product, process, and management innovations helps firms to respond to change faster than their competitors, and how SC and RC help the development of different types of innovations that fundamentally have different attributes (Subramanian and Nilakanta, 1996). Case studies can potentially delve below the surface of elucidated relationships and are useful in uncovering idiosyncratic attributes that define identified relationships. Fifth, this study adopted a self-assessment approach and collected data from key informants. Although this approach allows the collection of high-quality information on the constructs of interest, it is plagued by perceptual assessment problems, particularly common method bias (Kortmann, 2015). To completely preclude same-source bias, future research could collect data from multiple respondents (Kortmann, 2015; Kortmann et al., 2014).

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Sin-Pei Ng

Appendix

Measurement scales.

Construct	Item	Loading	AVE	CR	Cronbach’s Alpha
Sensing capability	In determining our strategic direction, we search for trends emerging outside our industry*		0.538	0.822	0.720
	Our strategy includes converting external trends into business opportunities.	0.672
	We detect changes in the external environment before most other firms.	0.818
	Our organizational objectives are directly influenced by trends in the business environment.	0.760
Reconfiguring capability	We periodically review our product/service development efforts to ensure that they are in line with what customers want.	0.879	0.713	0.908	0.863
	We review and redefine our marketing strategies if major competitors launch an extensive campaign targeted at our customers.	0.737
	We are able to rearrange our existing resources (such as human resources, financial allocations, and R&D) in new ways to respond to changing business environments.	0.854
	We are capable of combining technological improvements from several previously separate fields of technology to create products/services.	0.900
	For various reasons, we find it difficult to respond to changes in the marketplace. (reverse coded)*
	Even when we have great ideas, we find it difficult to implement them. (reverse coded)*
Product innovation	Newness (i.e., a product such as computers, software, and network solutions is new to the market).	0.825	0.700	0.942	0.927
	Productivity (i.e., a product such as computers, software, and network solutions increases a customer’s work efficiency).	0.880
	Reliability (i.e., a product such as computers, software, and network solutions is free of errors).	0.792
	Compatibility (i.e., a product such as computers, software, and network solutions is compatible with customers’ usages).	0.870
	Uniqueness (i.e., a product such as computers, software, and network solutions has unique features).	0.811
	Ease of use (i.e., a product such as computers, software, and network solutions is easy to learn and use).	0.851
	Functionality (i.e., a product such as computers, software, and network solutions meets customers’ functional needs).	0.823
Process innovation	Our firm has made major innovations in the processes used in the industry.	0.888	0.720	0.911	0.863
	Our firm has emphasized developing new methods and procedures to produce products or deliver services.	0.875
	Our firm allocates most of its R&D spending to develop new processes and technologies.	0.794
	We constantly adopt new technologies in new processes.	0.832
Management innovation	We are constantly improving the ways we control and direct departments of our firm.	0.808	0.654	0.883	0.823
	During the past 5 years, our company has developed many new management approaches (e.g., quality management, contingency plans, and key performance indexes).	0.796
	When we cannot solve a problem using existing business methods, we develop new business methods.	0.778
	We constantly adopt new technologies to improve our management approaches.	0.852
Timely responsiveness	It takes us very little time to respond to competitive pressure with a strategy of our own.	0.743	0.734	0.943	0.925
	We tend to execute a rapid response to changes in our customers’ product or service needs.	0.838
	In this firm, strategy implementation could be characterized as rapid.	0.886
	We are able to move quickly from strategy development to its use (or abandonment).	0.923
	Changes in our industry are quickly met with changes in our firm’s strategy.	0.858
	We are able to implement strategy in a timely fashion.	0.883

Note: *This item was eliminated during the scale purification phase. All indicators’ loadings are statistically significant at p<0.001 (2-tailed). AVE, average variance extracted; CR, composite reliability.

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