Results of structural equation modeling-model I.
Management scholars should further study the scientific area concerning the contingent effects of learning capability and organizational innovations on the relationship between quality management organizational performance. This chapter examines the interactive effects of quality management with organizational learning capability and innovations on organizational performance. Indeed, it may be argued that within quality management theory and methodology, the need to consider the contingency approach may result in an in-depth understanding of how the intersection of constituent elements associated with quality management influences organizational performance. Results revealed that the interaction of quality management and learning capability explained higher variance in organizational performance than the direct effect of quality management on performance. Similarly, interactions between quality management and innovations explained more significant variance in organizational performance than the direct effect of quality management on performance. Outcomes showed that quality management might not directly impact organizational performance. Findings underscore the importance of interactive effects of innovation and organizational learning capability with quality management in explaining the relationship between quality management and organizational performance.
- integrated quality management
- contingency theory
- learning capability
Organizations competing in dynamic industries are required to be cognizant of challenges and complexity in maintaining a balance between initiating changes through innovations and maintaining stability in their existing processes. Unpredictability within dynamic competitive markets creates a paradox between replicating stable processes or re-allocating resources toward innovation . Hence, organizational success tends to be contingent on organizational commitment and capability to continuously explore a new way of doing things and exploit existing competencies . Markets in dynamic industries tend to exert more significant pressure on competing firms to sense and respond to cues in their environment by creating flexible and adaptable core capabilities. The recent trends toward the adoption and implementation of total quality management have been indicative of competitive challenges in dynamic industries. As competitive advantage tends to erode at an accelerated pace , organizations that are responsive to intra-organizational cues and shifts in elements within the immediate organizational environment may have a better chance of success and prosperity [3, 4]. A healthy competitive position in the marketplace requires managers to coordinate among various internal processes, such as continuous improvements, innovations, and efficiency, through enhanced organizational learning capability. Moreover, internal coordination among process improvement, innovation, and organizational learning may lead to equilibria between continuous changes in various constituents in quality management and maintaining stability in existing processes. Integrated total quality management strategies enable managers to explore and implement a novel way of doing things and maintain stable and standard processes by repetition and duplication of high-performing processes. A number of researchers have posited that the performance outcome of quality management strategies tends to be contingent on the managerial capability to coordinate among timely innovations, investment in human capital, enhanced learning capability, and knowledge collaboration among organizational members and subunits [5, 6, 7, 8]. Moreover, integrated quality management enables organizations to exploit the existing core capabilities and channel organizational knowledge into individual and team cognitive energy to gain competitive advantage and enhance organizational performance e.g., [8, 9] and organizational excellence . Moreover, integrated quality management provides a window of opportunity for managers to detect and adapt to the external environment contingencies in a timely fashion . The inconsistency in the causal linkage between desired performance outcome [12, 13] and integrated quality management strategies and practices at the operational level remain inconsistent [14, 15]. Past studies have shown inconsistent results in the relationship between performance and integrated quality management. For example, research by Powell  and Westphal et al.  revealed no statistical significance between performance and total quality management. In contrast, few researchers have reported a direct and positive association [18, 19]) or a mediated relationship between organizational performance and quality management. Previous researchers have parsed and identified various components of integrated quality management and investigated each component’s relationship with performance.
In this body of work, the financial measure of organizational success , human resource capability , research and development were explored as firm-specific capability . Furthermore, integrated total quality management draws upon firm-specific resources and capabilities and coordinates a strategic balance between exploring new ideas and exploiting existing firm-specific capabilities [9, 21]. Such capabilities developed within integrated quality management tend to be non-imitable and sources of competitive advantage and higher performance [22, 23]. The causal ambiguity in the relationship between quality management and performance led to failures in the implementation of quality management . Furthermore, causal ambiguity in the quality management-performance relationship has refocused research studies on the interrelationship between constituent elements of quality management and organizational performance. For instance, research by Modarres and Pezeshk indicated that the relationship between total quality management and organizational performance is mediated by organizational learning and innovation performance. Similarly, Huang et al.  argued that individual interactions mediate the innovation performance in the quality management method and the degree of the team learning that may result from team member interactions.
Another body of research centered on the interrelationship between investment in human capital and success in the implementation outcome of quality management . Other researchers have discussed that the quality management-performance relationship tends to be contingent on creating a culture of dyadic trust among organizational members and promoting knowledge sharing among the organizational members . Both dyadic trust and knowledge sharing create an internal organizational environment that generates enhanced cognitive learning. Furthermore, knowledge sharing allows accumulated knowledge by members of the organization to become the basis for diverse ideas and explorations of novel routines. Within this body of research, the relationship between quality management and performance tends to be contingent on a culture of employee empowerment within organizations . Such a culture promotes an environment of learning and interaction, mutual trust, and information sharing among organizational members that may lead to the introduction of new products and services and the implementation of new codes in the organization.
Parsing quality management into its constituent parts and their synthetic roles within quality management have partially contributed to our understanding of the performance-quality management relationship. However, previous researchers have provided little information about the interactive effects of quality management with organizational learning and innovations to explain performance variations within corporations. This chapter derives from contingency theory to examine the contingency theory, neglected in recent quality management studies, to examine the interactions between quality management and two important variables, organizational learning, and innovations in explaining variations in organizational performance.
The proposed model (Figure 1) and hypotheses tested both direct and interaction effects between quality management, organizational learning, and innovation on various organizational performance levels. In contrast to parsing the constituent parts and their synthetic roles within quality management, the present research proposes that the interactions between quality management and learning capability and innovation tend to positively impact organizational performance. The present research views quality management as an integrated, gestalt, and adaptive method capable of continuously learning  and innovating novel routines and new core competencies. Furthermore, present research argues that integrated quality management allows for incremental modifications and radical reengineering of existing operations and enables managers to be flexible and enable the transformation and enhancement of internal capabilities.
2. Interaction effects of quality management with learning capability
Integrated quality management practices promote cross-functional communication and frequent exchanges of complex information among individuals and teams. Interaction between quality management and learning capability across subunits is likely to result in a novel way of doing things. Such knowledge creation commits top executives to allocate resources to employees’ education, expression of new ideas, and team learning.
Furthermore, the managerial challenge in establishing a stable and reliable process tends to be contingent on creating an organizational culture. Such culture focuses on creating new knowledge and continuous organizational learning and the existing experience curve accumulated through information flow across subunits . Such a seamless flow of information across subunits allows organizational members and managers to explore novel routines and exploit existing knowledge. Integrated quality management enables top managers to invest in continuous education and learning through employees’ interactions. Over time, the accumulated education and learning become the basis for organizational learning capability [25, 26] and the flexibility to explore new routines and continuous process improvement . According to Jerez-Gomez et al. , the interactions between top management commitment to employees’ education and employee involvement in strategic directions of the organization enhance learning as one of the organization’s core competencies. Moreover, higher levels of learning and education tend to lead to better implementation of quality management, greater innovation, higher quality of products and services, and higher organizational performance .
Moreover, high levels of learning capability within quality management enhance organizational awareness and ability to absorb new knowledge and transform the collective organizational know-how into new products and competitive advantage . In contrast, low adaptive learning and low organizational performance tend to be attributed to parochial organizational practices and the inability to absorb new knowledge . Similarly, the interaction between quality management and organizational innovations is likely to allow exploration for the opportunity to develop new products and services. Innovation tends to be among the success factors that contribute to high corporate performance [9, 22]. Previous researchers have argued that a positive association between innovation and organizational performance tends to be contingent on the flexible structural design that facilitates subunits innovations and interconnectedness, decentralized decision-making, and accumulated organizational learning [13, 30, 31, 32]. According to Singh and Smith , quality management practices promote an organic environment within organizations that is conducive to innovation and high levels of learning. Such organic structural design promotes employee interactions and cross-functional links and interactions. Furthermore, the organic structural design creates greater flexibility , that facilitates the speed and extent of innovations, and timely adaptation to changes in the firm’s industry environment.
Moreover, quality management practices that promote the timely introduction of products and services to the marketplace can lead to competitive advantage and high organizational performance . Similarly, entrepreneurial mindset within organizations tends to be a key factor in technological and product innovations. Furthermore, entrepreneurial mindset enables managers to respond to environmental changes by reallocating valued resources within the organization toward new products and services and enhancing corporate performance [22, 30, 35, 36]. Finally, quality management creates a culture of collaborations and exchanges of new ideas as employees interact within each function and cross-functionally. Researchers must identify the interrelationship among quality management, learning capability, and innovations to realize a deeper understanding of how employee interaction may lead to higher organizational learning capability and innovations. Furthermore, research studies should explore the interactive effects of quality management, learning capability, and innovation on organizational performance. Given the above, this study hypothesizes the main and intersection effects between integrated quality management, organizational learning, and innovations in the following manner:
3.1 Sample and data
3.2 Measurement of variables
A survey method was used for all the variables in the present study. Respondents were asked to indicate their levels of agreement with descriptive statements using a 5-point Likert scale (range, 1 = strongly disagree to 5 = strongly agree).
top management support
education and training
3.3 Procedures and design
Congruent with the previous research in contingency theory , the present research considers quality management as an integrated organizational strategy. As such, the study used structural equation modeling to explore the independent and interaction effects of integrated quality management, innovations, and organizational learning on organizational performance. For parsimony, and to reduce the number of relationships, a hierarchical component model was created. Model I (Table 1, Figure 1) shows the results of the structural equation modeling analysis of the high component model, and standardized regression weights showing integrated quality management association with organizational learning capability, products and services innovations, and organizational performance. The hierarchical analysis of Model I also shows the relationship between each of the four constructs in this study with their sub-constructs.
|Standardized regression weight||Standardized bias||t-value|
|Quality management ➔ Organizational learning||0.95||0.08||13.41*|
|Quality management ➔ Innovation performance||0.91||0.08||12.41*|
|Quality management ➔ Organizational performance||0.43||0.08||1.13|
|Quality management ➔ Education and training||0.90||0.08||14.20*|
|Quality management ➔ Total management support||0.72||0.08||10.41*|
|Quality management ➔ Continuous improvement||0.61||0.08||8.60*|
|Quality management ➔ Supply chain mgt||0.55||0.08||7.67*|
|Quality management ➔ Customer focus||0.45||0.08||6.29*|
|Quality management ➔ Employee involvement||0.41||0.08||6.21*|
|Organizational learning ➔ Management commitment||0.84||—||—|
|Organizational learning ➔ System perspective||0.71||0.08||10.34*|
|Organizational learning ➔ Organizational experiment||0.66||0.08||9.31*|
|Organizational learning ➔ knowledge transfer||0.83||0.08||8.21*|
|Organizational learning ➔ Organizational performance||0.58||0.08||6.89*|
|Innovation performance ➔ Product/service||0.92||—||—|
|Innovation performance ➔ Process innovation||0.78||0.08||12.24*|
|Innovation performance ➔ Overall organizational innovation||0.79||0.08||12.57*|
|Innovation performance ➔ Organizational performance||0.62||0.08||9.17*|
|Innovation performance ➔ Product/service||0.92||—||—|
|Innovation performance ➔ Process innovation||0.78||0.08||12.24*|
|Innovation performance ➔ Overall organizational innovation||0.79||0.08||12.57*|
|Innovation performance ➔ Organizational performance||0.62||0.08||9.17*|
|Organization Performance ➔ Post TQM financial expectation||0.65||—||—|
|Organization Performance ➔ Employee participation||0.63||—||—|
|Organization Performance ➔ Customer satisfaction||0.59||0.08||7.79*|
|Organizational performance ➔ Employee satisfaction||0.75||0.08||8.57*|
|Organizational performance ➔ Sustainability||0.92||0.08||13.16*|
4.1 Main constructs, sub-constructs, and variables
For the accuracy of the constructed model and to make sure the data is presenting accurate and reliable drawing from the population under study the Kolmogrov-Smrinov (KS) test was performed [40, 41]. Table 2 shows that all four variables’ data are normally distributed.
|Most Extreme Difference||Absolute||0.059||0.059||0.076||0.057|
|Asymp. Sig (2-tailed)||0.687||0.676||0.358||0.726|
5. Explanation of latent constructs
In this section of the chapter, complex hierarchical constructs, sub-constructs, and related subset variables are disentangled and discussed.
5.1 Integrated quality management
Table 3 presents the result of an orthogonal (VARIMAX) rotation of the factor matrix underlying the quality management items. Based on the six-independent factor solution suggested by the eigenvalue pattern (i.e., greater than 1.0), 25 items were identified so that each of which loaded at least cleanly on only one of the six factors. A cut-off of 0.50 was used for item-scale selection. These factors accounted for over 78% of the variance in the quality management scale items. Following an inspection of the factor loadings, the six factors were subsequently labeled:
Total management support
education and training
continuous improvement and innovation
supply chain management
|Factors||Cronbach’s alphas||Scales included|
|b1 Employee involvement||0|
|b2 Total Management Support||0|
|b3 Supply Management||0|
|b4 Continuous improvements||0|
|b5 Customer Focus||0|
|b6 Education Training||0|
Table 4 shows an examination of the Kaiser-Meyer Olkin measure of sampling adequacy suggested that the sample was factorable. The results reasonably describe each set of items as being indicative of an underlying factor for quality management.
|Kaiser-Meyer-Olkin Measure of Sampling Adequacy||0.833|
|Bartlett’s Test of Sphericity||Approx. Chi-Square||3485|
(KMO = 0.833); χ2 = 3485, df, 300, sig 0.000).
Results of second-order confirmatory factor analysis (Table 3) present the scale reliability on quality management dimensions that reached statistical significance. This indicates that criteria had a significant correlation with appropriate dimensions and scales had convergent validity .
Findings (shown in Table 5) also indicated that integrated quality management is positively and significantly associated with human resource development through continuous education and training (
|Total Quality Management-QM|
|1. Top managers’ commitment to training employees in quality management||0.96||/a||0.94|
|2. Top managers training in best conduct with employees and customers||0.67||10.36*||13.32*|
|3. Employees knowledge about food industry||0.95||15.66*|
|4. Managers’ commitment to providing employees essential needs at work||0.76||13.27*|
|1. Top managers’ commitment to post-implementation of quality management||0.86||/a||0.73||8.64*|
|2. Top managers’ commitment to long-term investment in quality management||0.91||15.65*|
|3. Top managers’ support of employee involvement in quality management implementation||0.88||14.51*|
|4. Top managers’ strategic co-alignment of quality management with changes in market||0.98||16.49*|
|1. Employees are encouraged to make suggestions about work condition improvements||0.88||/a||0.70||8.23*|
|2. Employees are encouraged to research to improve products and services||0.75||11.21*|
|3. Manager’s consideration of suggestions for product/services improvement||0.94||15.44*|
|1. Coordination with the critical supplier through information sharing||0.88||/a||0.70||8.27*|
|2. Enhance the quality of suppliers post quality management implementation||0.86||13.84*|
|3. Establish a win-win relation with suppliers||0.78||11.76*|
|4. Strategic view on managing supply-chain||0.86||13.83*|
|1. Center firm activities based on customer satisfaction||0.84||/a||.54||6.02*|
|2. Customer satisfaction and expectation as a top goal||0.83||11.52*|
|3. Importance of customers in top managers’ decisions||0.88||12.25*|
|1. Employee training and encouragement to participate in company programs||0.57||/a||0.33||3.50*|
|2. Creation of work improvement teams||0.96||7.09*|
|3. Employees suggestions about improving supply-chain||0.96||7.99*|
|4. Employees responsibility to inspect work outcome||0.66||6.47*|
|5. Creation of quality circles to assist staff in problem-solving||0.70||6.71*|
|6. Employee participation in management quality programs||0.75||7.00*|
|7. Establishing a reward program for novel suggestions by employees||0.82||7.36*|
5.2 Organizational Learning capability
Results of an orthogonal (VARIMAX) rotation of the factor matrix (Table 6) indicate underlying organizational learning capability items. Based on the four-independent factor solution suggested by the eigenvalue pattern (i.e., greater than 1.0), 15 items were identified so that each of which loaded at least cleanly on only one of the four factors. A cut-off of 0.50 was used for item-scale selection. These factors accounted for over 75% of the variance in the organizational learning capability scale items. Following an inspection of the factor loadings, four factors were subsequently labeled “management commitment,” “system perspectives,” “organizational experiment,” and “knowledge transfer initiative.” After the initial component analysis number of items was reduced to 15 which explained the highest variation in organizational learning.
|Organizational Learning Capabilityb||MCb1||SPb2||OEXb3||KTIb4|
Table 7 shows the Kiser-Meyer-Olkin, and Bartlett test of sphericity utilized to measure four organizational learning dimensions, with each of the dimensions being measured by responses to several items. The results reasonably describe each set of items as being indicative of an underlying factor for learning capability (KMO > 0.818; χ2 = 1843, df, 120, sig 0.000).
|Kaiser-Meyer-Olkin Measure of Sampling Adequacy||0.818|
|Bartlett’s Test of Sphericity||Approx. Chi-Square||1843|
Results of second-order confirmatory factor analysis (Table 6) present the scale reliability on organizational learning dimensions that reached statistical significance. This indicates that criteria had a significant correlation with dimensions and scales had convergent validity .
Furthermore, results (shown in Table 8) indicated that organizational learning capability positive and significant relationship with management commitment to long-term investment in human resources development and organizational learning (
|Organizational Learning Capability|
|1. Employee participation in management decision making||0.81||/a||0.88||0.930*|
|2. Invest in employee learning||0.78||10.36*|
|3. Embracing change to adapt to changing business environment||0.73||9.66*|
|4. Employee learning as a key success factor in company||0.77||10.33*|
|5. Rewarding novel ideas||0.86||11.89*|
|1. Job expansion through creativity and experimentation||0.85||/a||0.80||9.0*|
|2. Adopting best practices in competitive field||.84||12.60*|
|3. Considering expert views outside company to improve learning||0.85||12.64*|
|4. Creating a culture of accepting ideas generated by employees||0.76||10.84*|
|1. Employee knowledge about the strategic direction of company||0.83||/a||0.72||7.96*|
|2. Divisional participation in company goals||0.88||13.19*|
|3. Communication among company divisions/departments||0.94||14.30*|
|1. Discussion about shortcomings and mistakes at all levels||0.82||/a||0.63||6.66*|
|2. Discussions about ideas, programs, and activities among employees||0.83||10.43*|
|3. Culture of teamwork||0.44||5.14*|
|4. Maintenance of work process documentation||0.78||9.90*|
5.3 Organizational innovation
Table 9 presents the result of an orthogonal (VARIMAX) rotation of the factor matrix underlying organizational innovation items. Based on the three-independent factor solution suggested by the eigenvalue pattern (i.e., greater than 1.0), 17 items were identified so that each of which loaded at least cleanly on only one of three factors. A cut-off of 0.50 was used for item-scale selection. These factors accounted for over 74% of the variance in the organizational innovation scale items. Following the factor loadings, the three factors were subsequently labeled “product/services initiatives,” “product innovation,” and “overall organizational innovation.”
Table 10 shows the Kiser-Meyer-Olkin and Bartlett test of sphericity. Results reasonably describe each set of items as being indicative of underlying factors for organizational innovation (KMO > 0.891; χ2 = 2.418E3, df, 136, Sig, 0.000). Furthermore, results are indicative of a relationship among the innovation components, “product innovation,” “process innovation,” and “organizational innovation.”
|Kaiser-Meyer-Olkin Measure of Sampling Adequacy||0.891|
|Bartlett’s Test of Sphericity||Approx. Chi-Square||2.418E3|
Table 9 shows the results of second-order confirmatory factor analysis and the scale reliability on organizational innovation dimensions that reached statistical significance. This indicates that criteria had a significant correlation with dimensions and that the scales had convergent validity . Results (Shown in Table 8) were also indicative of a significant and positive correlation between innovation and the introduction of new products and services (
|Overall Organizational Innovation|
|1. Higher rate of innovation in comparison to competitors||0.79||/a||0.92||9.88*|
|2. Higher production improvement in comparison to competitors||0.82||11.32*|
|3. Faster acquisition of innovative ideas compare to competitors||0.94||13.55*|
|4. Knowledge and skill improvement through R&D||0.72||9.56*|
|5. Production of products that better fit customer needs||0.92||13.21*|
|6. Introduction of new products to customers faster than competitors||0.95||10.10*|
|1. Utilizing novel ideas to improve the product quality and speed of deliver||0.88||/a||0.78||10.25*|
|2. Utilizing quality resources in the production process||0.80||12.61*|
|3. Flexibility in resources allocation||0.68||9.72*|
|4. Cost reduction through efficient resource allocation||0.78||11.86*|
|5. Adoption of human resources management||0.81||12.86*|
|6. Flexibility in org-structure compare to competitors that allows innovation||0.89||15.42*|
|1. Best use of organizational resources to implement quality management||0.77||/a||0.77||8.28*|
|2. Unit cost reduction after implementation of quality management||0.84||11.08*|
|3. Financial improvement after quality management improvement||0.81||10.61*|
|4. Increased employee productivity after quality management implementation||0.79||10.18*|
5.4 Organizational performance
Table 12 presents the result of an orthogonal (VARIMAX) rotation of the factor matrix underlying organizational performance items. Based on the four-independent factor solution suggested by the eigenvalue pattern (i.e., greater than 1.0), 16 items were identified so that each of which loaded at least cleanly on only one of four factors. A cut-off of 0.50 was used for item-scale selection. These factors accounted for over 77% of the variance in the organizational performance scale items. Following an inspection of the factor loadings, the four factors were subsequently labeled “customer satisfaction,” “employee satisfaction,” “environmental performance,” and “environmental sustainability.”
Kaiser-Meyer-Olkin and Bartlett test of sphericity (shown in Table 13) was utilized to measure four organizational performance dimensions, with each of the dimensions being measured by responses to several items. Results (shown in Table E) reasonably describe each set of items as being indicative of an underlying factor for organizational performance (KMO > 0.862; χ2 = 1.971E3, df, 120, Bartlett’s Test of sphericity with significant of 0.000 (less than 0.05).
|Kaiser-Meyer-Olkin Measure of Sampling Adequacy||0.862|
|Bartlett’s Test of Sphericity||Approx. Chi-Square||1.971E3|
Table 12 shows the results of second-order confirmatory factor analysis and the scale reliability on organizational performance dimensions that reached statistical significance. This indicates that criteria had a significant correlation with dimensions and that the scale had convergent validity .
Results of path analysis indicated top echelon focus on reduced turnover rate by instituting a high remuneration policy and employee satisfaction (
|1. Employee satisfaction||0.86||/a||0.75||10.88*|
|2. Ample remuneration for employees||0.87||14.12*|
|3. Reducing turnover after quality management implementation||0.88||14.37*|
|4. Reduction of absenteeism after quality management implementation||0.83||12.97*|
|1. Customer satisfaction||0.86||/a||0.75||10.88*|
|2. Introduction of new product and services||0.80||10.91*|
|3. Reduction of product defect returns after quality management implementation||0.75||10.03*|
|4. Strategies to maintain customer base||0.82||11.41*|
|5. Higher profitability after quality management implementation||0.88||11.74*|
|6. Reducing customer complaints after quality management implementation||0.89||12.65*|
|1. Consideration of environmental projects after implementation of quality management||0.74||/a||0.63||7.41*|
|2. Sustainability/Reducing production pollution||0.85||9.31*|
|3. Reducing complains about environmental pollution||0.75||8.56*|
6. Results and discussion
A detailed analysis revealed that organizational learning capability is positively and significantly associated with organizational performance (
Findings also supported hypotheses H2 and H3.
6.1 Interaction effects
As managers attempt to identify factors that influence organizations’ performance, this research argued that it is important to gain a deeper understanding as to how interaction effects of quality management, learning capability, and innovations matter in influencing organizational performance. The hypothesis H4 specified that organizational performance would be affected by an interactive effect of quality management and organizational learning capability. The hypothesis H5 specified that organizational performance would be affected by an interactive effect of quality management and innovation. To test these hypotheses, I employed structural equation modeling analysis to reduce the number of variables and to capture the interrelations of measured variables and latent constructs, as suggested by Tarka . Results indicated that compared to the effects of quality management and organizational performance (
There are several important theoretical and practical implications that emerge from this research. Findings underscore the importance of the interaction of quality management elements. Over the past decade, researchers have systematically underplayed the interaction effects of quality management elements. The present research showed that the dominant impact on organizational performance, beyond external resource considerations, is the intersection of forces associated with quality management, organizational learning capability, and innovations within these organizations. It was argued earlier that within quality management theory and methodology, the need to consider the contingency approach might result in an in-depth understanding of the strategic allocation of resources and managing and coordinating among the interrelated constituent elements within quality management. Results suggested that organizational performance is positively influenced by the interaction of quality management and innovation and learning capability at organizational levels. It is also clear that there are distinct differences between parsed and integrated constituents within quality management with respect to explaining variations in organizational performance. This finding is of some theoretical significance.
As a strategy, quality management appears to have coordination challenges associated with learning capability and application of such learning to innovations of new products and services. This study found that organizational performance is significantly impacted by the interaction between quality management and learning capability. Similarly, findings indicated that interaction between innovation and quality management positively and significantly influences organizational performance. The strength of these findings, particularly in light of incorporating external environmental factors such as sustainability considerations, points to the potential importance of revitalizing the contingency theory perspective pertaining to integrated quality management. Such a revival would not necessarily imply that researchers “pit” internal elements influencing performance against external forces. Instead, more direct integration of contingency variables within quality management is suggested to better balance internal and external perspectives on organizational performance. Nevertheless, any resurrection of this perspective within quality management theory and methodology may require changes in how contingency theory may be employed (e.g., Pfeffer 1997). This study did not limit its focus to examining the main effects of organizational learning capability, innovations, and quality management on performance. As I argued in theoretical development, one cannot easily specify the nature of these main effects. Instead, what may be as, if not more, important to consider is the interaction of these variables as previous organizational researchers have argued that internal and external characteristics of organizations and their members may cluster together in predictable patterns to explain a variety of micro to macro-level organizational processes and relationships . Congruent with Meyer et al.’s findings on organizational learning capability showed top managerial commitment to implement a complex set of policies on the development of human resources. Such policies included learning based on system perspectives, learning associated with experimentation and exploration of a novel way of doing things, and knowledge transfer at various levels of individuals, teams, and organizational subunits. Furthermore, findings revealed managerial efforts to coordinate and co-align subunits’ strategies with the organization at the macro level. Similarly, findings on innovations showed managerial commitment to implementing flexible resource allocation strategies for subunits to explore novel processes and ideas. Findings were congruent with the notion that integrating interrelated constituents of quality management at the micro and macro level require greater structural flexibility and high levels of coordination among organizational activities. While the explicit consideration of interactive variables in quality management theory adds complexity to the understanding and application of contingency theory, this type of complexity is what managers must face. Rarely, is there the luxury of focusing exclusively on one aspect of quality management, as has been the themes of previous research, in isolation from others? For the contingency theory to develop as a theoretical perspective and be relevant to the practical concerns of managers and executives, researchers may need to provide further attention to how constructs in quality management and their subset variables interact to influence organizational performance over time.
Employing contingency theory to conduct future research in the quality management field will also require making more direct connections between the results of studies and the organizational design concerns of managers. One important vehicle for doing this is by considering how quality management research findings can be connected to process considerations at various levels of organization. It is often organizational processes that are of most direct concern to managers adopting quality management practice. Perhaps, the most direct implication relates to the enhanced importance of managing and integrating complex processes within and between each constituent of quality management.
Therefore, it is critical for an organization adopting quality management to develop an organizational capability or competence for managing internal complex and interrelated process models. Without this capability, managerial policies and efforts can become misguided and create greater conflict, thereby undermining the effectiveness of coordination efforts among complex processes to achieve timely policy and strategy adjustments. Successful corporations such as Boeing and car manufacturers recognized the need to employ quality management and, as the corporation evolves, developed organizational capabilities to manage complex processes.
Future researchers may wish to create a matrix that examines the contingent effects of long-term variations in learning capability on innovations and assess variations in long-term innovations on organizational performance.
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