ationship with suppliers in order to encourage their valuable input towards the performance of innovation  . Long-term collaboration and partnership with suppliers would build trust between the two parties, which in turn facilitates effective communications and fosters a transparent working relationship. Working together in such a trust-based environment, the two parties have no worries of calculated behavior or betrayal, and gain better communication and the possibility of knowledge transfer  . It is clear that the benefits also include learning and knowledge sharing for both parties, potential new knowledge, and creative proposals.
To summarize, another set of hypotheses is proposed, based on the discussions above:
H3: The core QM practices have a positive effect on fostering product innovation performance.
H4: The core QM practices have a positive effect on fostering process innovation performance.
2.2. The Impact of Contextual Variables on QM Practices and Innovation
2.2.1. Contextual Variable
Contingency theory claims that the performance of a firm depends upon various internal and external variables   . Such contextual variables, in QM practice, are dynamic in nature. The literature indicates that the contextual variables that affect the successful introduction of QM practices to a firm include organizational factors  , managerial factors  , external factors  , and others. Given that firms exist in a certain environment, the external factors are regarded as critical moderating variables in organization theory and strategy management  . With the advent of globalization, networking, and fierce competition, customers’ needs turn out to be increasingly diverse and uncertain, which in turn causes the market to show certain features of turbulence. Here, the term “turbulence” refers to changes that emerge without any predictability or fixed model. Given the importance of market turbulence as a critical indicator of the external environment, this study attempts to explore its moderating effect on the relationship between QM practices and innovation performance.
2.2.2. The Moderating Effect of Market Turbulence on QM Infrastructure Practices and Innovation Performance
In a turbulent market, customers’ needs and preferences change frequently, as do the competitors, so that it becomes challenging for a production line to keep up with customer’s changing needs  . To avoid this, positive measures are needed to make a more reliable prediction by analyzing customers’ needs  . In such circumstances, top management commitment seems to be prominently important, because their commitment creates an environment of psychological security, thus minimizing employees’ fears, and encourage them to be more adventurous and experimental in their work. Furthermore, tangible resources like people, capital, facilities, and social emotional support are needed for innovation in a turbulent market, while leaders’ commitment and support is crucial for acquiring these necessary resources. Hence, in a market environment with high uncertainty, top management commitment is helpful for fostering product and process innovation.
Additionally, in turbulent markets, firms are likely to be confronted with various unstructured needs, so that the conventional method of market analysis and product development is difficult to sustain with the swift changes that occur. Therefore, the customer-focused approach seems appropriate and useful in monitoring, acquiring, and analyzing customers’ needs  . With the assistance of rapid resource allocation (aggressive behavior) and continuous experiments (proactive behavior), this approach is likely to enable new products and new services to be developed in a tight window prior to changes in customers’ needs  , with the objective of creating value for customers. Likewise, a market with a high level of uncertainty will also present more uncertainty and changeability, which the innovation process needs to cope with  . However, the involvement of employees promotes their adaptability and initiative in such challenges. Market uncertainty is likely to be analyzed and predicted during paradigm shifts, so that organizational innovation performance is promoted. Also, in market environments with high uncertainty, attention should be paid to learning-oriented practices when QM practices are introduced; such a priority is claimed to benefit firms’ innovation performance as well  . In summary, in turbulent market environments, there will be a greater impact on the performance of product and process innovation, exerted by leader commitment, customer focus, and employee involvement in the QM infrastructure practices.
On the other hand, under a stable market environment, customer needs and their preferences―along with technological development―change at a slower pace, so that competitors’ behaviors can be relatively easily predicted, and firms’ production lines can match customer needs. Furthermore, the existing methods or frameworks of QM practice are sufficient to handle the problems that firms encounter. As such, the impact of QM infrastructure practices on innovation, the commitment of leaders, customer focus, employee involvement, and others, are less obvious. For instance, customers’ needs are easier to predict as firms adopt the customer-focus strategy, and so uncertainly decreases significantly in a mature or stable market environment. Likewise, because mature-market risk and uncertainty are reduced, the effects of leader commitment and support (especially for social emotional support) on innovation will be weakened. Based on the above analysis, following hypotheses are proposed:
H1a: The relationship between QM infrastructure practices and product innovation performance is moderated by market turbulence. The higher the level of market turbulence, the more significant the positive impact of QM infrastructure practices on product innovation performance.
H2b: The relationship between infrastructure practices and process innovation performance is moderated by market turbulence. The higher the level of market turbulence is, the more significant the negative impact of QM infrastructure practices on process innovation performance will be.
2.2.3. The Moderating Effect of Market Turbulence on Core QM Practices and Innovation Performance
When the market environment involves high uncertainty, customers’ expectations and preferences change in a rapid manner. To be competitive, it is necessary that firms acquire customers’ new needs. However, satisfying customers’ changing needs with new products continuously being developed is very challenging, as “discontinuity innovation” does not work well with continuous improvement  , which is based on the existing knowledge base. This may result in firms being in a disadvantageous position, failing to respond to the rapid changes of the external market environment  . Successful experience and models in prior processes of continuous improvement might also be hindered in a turbulent environment, so that the innovation performance of product and process would be affected. Similarly, in market environments characterized by high uncertainty, standardization-focused process management might decrease ambiguity (a crucial factor of innovation), making innovation more difficult  . This is because, by nature, repetitive routines refer to the documentation of SOPs, and repetition does not benefit firms at all in searching for and acquiring the changing needs of customer and new knowledge  . Consequently, product development and innovation will be affected in such turbulent market environments. Also, market environments with frequent changes increase the difficulty level and cost of maintaining a good supplier relationship. This is because cost pressure can negatively impact the trust between the two parties. Again, this would not help firms in terms of acquiring knowledge from suppliers―in particular, tacit knowledge, professional skills, or sensitive information that can only be shared in a mutual trusting working environment  . This would ultimately affect firms’ innovation capacity and innovation performance. To summarize, it could be concluded that, in turbulent market environments, the positive impact of the core QM practices―including continuous improvement, process management, and supplier relationships―on product and process innovation performance will be significantly affected.
On the other hand, in market environments with low uncertainty, customers’ preferences and technological development are relatively stable, so that what firms are faced with is structural need  . With respect to this type of demands firms are able to satisfy customers’ needs through continuously improving their products and services so as to respond to the changes from the external market environment. Likewise, in a steady market environment, repetitive routines and SOPs built in process management are capable of supporting innovation activity, which promotes radical innovations of product and process  . Besides, the steady market environment is more favorable for established supplier relationships, which enables firms to acquire the technological resources and knowledge needed for innovation. Also, a trusted relationship would facilitate a more interactive and constructive dialogue between the two parties, and is also helpful in overcoming the “Not Invented Here” symptom  . Consequently, firms’ innovation will be improved. This suggests that the moderating effect of a steady market environment will have a positive effect on the relationship between the core QM practices and the innovation performance of product and process. To summarize, the following hypotheses are proposed:
H3a: The relationship between core QM practices and product innovation performance is moderated by market turbulence. The lower the level of market turbulence, the more significant the positive impact of core QM practices on product innovation performance.
H4b: The relationship between core practices and process innovation performance is moderated by market turbulence. The lower the level of market turbulence, the more significant the positive impact of core QM practices on process innovation performance.
The conceptual model is formulated in Figure 1.
3. Research Design
3.1. Sample and Data Collection
Having taking cost, convenience, and geographic factors into account, the target sample for this study consisted
Figure 1. Research conceptual model.
of Chinese manufacturing firms from different provinces, with a focus on Zhejiang, where the local governments have heavily pushed the quality management initiative and awards. Questionnaire surveys were adopted for data collection, and were sent out to firms through various means, including e-mail and via personal contacts, from December 2011 to June 2012. Details of sampling are shown in Table 1. The primary industry surveyed was machinery manufacturing (34.42%), followed by chemical industry (20%), electronics and communication (13.49%), and others.
The target respondents included general managers and top-to-middle managers with at least three years’ working experience. The innovation performance section of the questionnaire required the input of top-to-mid- dle managers, and the quality management practice part required answers from quality managers. By June 2012, 254 responses were obtained, of which 39 questionnaires were incomplete, giving a 56.14% success rate.
The design of questionnaire survey was carried out rigorously, beginning with an extensive literature review of QM practices, innovation, and others, with a focus on measurements. To suit the Chinese context, the study involved a series of questionnaire trials, in which three well-educated quality managers were invited to assess the logic between, and applicability of, the variables and measures. This was followed by inviting five top-to-middle managers to assess the clarity and comprehensibility of the questionnaire. The result was the identification of 15 items for measuring QM infrastructure practices, 12 core practice related items, 4 items each pertaining to product and process innovation, and 4 items relating to market turbulence. All questions are in the form of a Likert scale, with respondents choosing a number from 1 to 7 to determine their degree of agreement or disagreement with a statement  .
QM practices: this study was in agreement with the work done by Barbara B Flynn et al. (1995) and by Prajogo and Sohal (2001) on QM constructs   . We decided to use top management commitment, employee involvement, and customer focus as the three constructs for measuring core QM practices. On the other hand, continuous improvement, process flow management, and supplier relationship were employed to measure QM infrastructure practices.
Innovation performance: this variable was developed based on Martínez-Costa and Martínez-Lorente (2008) and Kim et al.’s (2012) research   , looking at the aspects of product and process innovation.
Market dynamics: this indicator was basically derived from the work of Hult et al. (2004) and of Maria Leticia Santos-Vijande and Alvarez-Gonzalez (2007)   . Four indicators were thus adopted, including customer preference, market competition, and new customers’ requirement. In this study, market turbulence variables were adopted from Hult et al. (2004) and Santos-Vijande et al.’s (2007) study   , and included rapidly changing buyer preference, wide-ranging needs and wants, and buyer entry and exit from the marketplace (market competition).
Table 1. Sample firms’ characteristics.
3.3. Reliability and Validity Test
Overall, as shown in Table 2, scale reliability is high. All the items exceeded the usual recommendation of alpha = 0.70 (Nunnally & Bernstein, 1967) for establishing the internal consistency of the scale  . Moreover, content validity and construct validity were both considered in this study. Content validity refers to the adequacy with which a specific domain of content is reflected in the measurement items of an instrument  . It is subjectively judged by the researcher. Given the fact that all the identified items were derived from the prior literature, and went through a trial with professional, it therefore can be considered to have content validity. Construct validity measures the extent to which the items in a scale measure the same construct   . Factor analysis was employed to evaluate the construct validity of each construct measure. The results in Table 2 show that item loading rage for each factor was relatively high. Thus, construct validity was demonstrated.
3.4. Correlation Analysis
This study employed SPSS 16.0 to analyze the descriptive statistics, correlations, and hierarchical regression. Table 3 reports the means, standards deviations, and coefficients of correlation for the variables. As Table 3 shows, the correlation coefficients for the measurements were highly related, ranging from 0.18 to 0.381. This
Table 2. Internal consistency analysis for all items.
Table 3. Correlations among the variables.
Note: N = 215; *p < 0.05 level, **p < 0.01 level (two-tailed).
indicates that QM infrastructure practices and core practices had positive and significant relations to product and process innovation performance. One negative relationship was found: that between market turbulence and core practice (r = −0.109, p < 0.05).
4. Regression Analyses
Hierarchical regression was employed in this study to test the research hypotheses. For a start, the variation inflation factors (VIF) of each measurement were obtained; for QM infrastructure practices, core QM practices, and market turbulence, the VIF values are 1.241, 1.105, and 1.326 respectively―all of which are greater than 1. This finding indicates that the models are not affected by multicollinearity problems  . In order to assess and validate the independence of the error assumptions, Durbin-Watson statistics were adopted. The value is 1.916, which lies close to 2, indicating that no autocorrelation issues were found. Further, this study employs the following four procedures to test the hypothesis: Enter the control variables into the regression equation in step 1 (i.e., model 1), three predictors in step 2 (i.e., model 2), one moderate factor in step 3 (i.e., model 3); and two two-way interactions in step 4 (i.e., model 4). For each step, the coefficients R2 and ΔR2 are evaluated.
4.1. Result for Product Innovation Performance
Table 4 shows the results of the regression analyses (models 1 to 4). Firstly, three control variables (size, nature, and age of firm) are entered into step 1. This shows that these three control variables explained only 8.5 percent of the product innovation performance (R2 = 0.085). Also, out of the three control variables, only the firm’s age was found to have a significant impact on product innovation performance. All these findings show that the control variables have a weak influence on the dependent variable.
Secondly, two variables of quality practices―namely QM infrastructure practice and core QM practices―were entered into step 2 as two predictors. Model 2 showed that the additional variance explained was 14.5 percent (p < 0.01). It was also found that QM infrastructure practice and core QM practices had positive and significant mean effects on product innovation performance (p < 0.01). This finding supports the hypotheses H1
Table 4. Result of hierarchical regression analysis (dependent variable: product innovation performance).
Note: Standardized regression coefficients are shown in the table; the values of ΔR2 in models 2, 3, and 4 are compared against model 1; *p < 0.05, **p < 0.01.
and H3. Thirdly, when the moderator factor (MT) is added into Model 2, the additional variance explained increased significantly to 46.8 percent of the explanations of variance in product innovation performance (ΔR2 = 0.383, p < 0.01). Lastly, as Table 4 shows, when the two-way interactions were entered, the increase in R2 from model 3 to model 4 was 0.46, which is statistically significant (ΔR2 = 0.462, p < 0.01). The results also showed that the coefficient for the two-way interaction effect of market turbulence and infrastructure practices was positive and significant (b = 0.224, p < 0.01), whilst market turbulence and core practices were negative and insignificant. Overall, the results suggest that the market turbulence factor positively moderates the impact of QM infrastructure practices on product innovation, but its moderating effect on the relationship between core QM practices and product innovation performance is not significant. Hence, H1a is justified, but H3a failed to be accepted.
4.2. Result for Process Innovation Performance
Following the above methods and steps of the data analysis, Table 5 reports the results of the regression analysis on process innovation performance conducted by QM practices, market turbulence, and the two-way interactions. Similarly, the results show that firm age is significant for process innovation performance, whilst the explanatory power of the other two control variables regarding process innovation performance is relatively low. Next, two QM practices are introduced to test the impact of the independent variable on the dependent variable when the other variables of the firms’ characteristics are controlled for. The results show that Model 6 accounts for 23.4% of process innovation performance, and its explanatory power is higher than that of Model 5 (ΔR2 = 0.129, p < 0.01). Regression analysis of Model 6 shows that QM infrastructure practices and core QM practices exert a positive and significant impact on process innovation performance (p < 0.05). Thus, hypotheses 2 and 4 are confirmed.
This is followed by introducing a moderating variable into the regression of Model 6. With market turbulence taken into consideration, the results show that Model 7 accounts for 41.7% of process innovation performance, with its explanatory power being further improved compared to model 6 (ΔR2 = 0.312, p < 0.01). Additionally, with interaction variables added, Model 8 accounts for 52.4% of product innovation performance, with its explanatory power higher than that of Model 7. Regression analysis indicates that the interaction effect of QM infrastructure practices and market turbulence is not significant (b = 0.087); the interaction effect of core QM
Table 5. Result of hierarchical regression analysis (dependent variable: process innovation performance).
Note: Standardized regression coefficients are shown in the table; the values of ΔR2 in models 6, 7, and 8 are compared against model 5; *p < 0.05, **p < 0.01.
practices and market turbulence achieved a significant level, with a native regression coefficient of interactions (b = −0.121, p < 0.05). The results show that the moderating effect of market turbulence on the relationship between infrastructure practices and process innovation performance is not significant, but that market turbulence could moderate the relationship between core practice and process innovation performance in a negative way. Thus, the implication is that hypothesis H2b fails to be accepted, while hypothesis H4b has been confirmed.
5. Discussion and Conclusion
This study has investigated the impact of QM practices on innovation performance in the Chinese context. It contributes to the quality management and innovation practice body of knowledge by identifying the moderating effects of market turbulence and the relationship between firm QM practices and innovation performance. The hypothesis was tested based on the empirical data from a sample of 383 firms across 9 Chinese provinces and cities, and the main conclusions drawn are as follows:
Firstly, it was found that, in the Chinese context, the local practices of quality management in the form of QM infrastructure practice and core QM practice had a significant positive effect on innovation performance    . This is in line with the findings of Prajogo and Sohal (2001), Choo, Linderman, and Schroeder (2007), and Kim et al. (2012), but inconsistent with the observations of Atuahene-Gima (1996), Slater and Narver (1994) on the negative relationship between the two   . Given that QM infrastructure practices prioritize key practices, such as top management commitment, customer focus, and employee involvement, which would help to cultivate a pro-innovation working environment, innovations are thus more likely to occur. This is because, working under such condition, individuals are motivated and gain the desire to innovate their working methods  . This also enhances their recognition for innovation and mobilizes firms’ innovation performance. Moreover, continuous improvement also encourages creative minds and learning, and is beneficial in that it allows repeated processes to gain opportunities for improvement on an incremental basis  . All of these will have effect on innovation performance  .
Secondly, it has also been found that market turbulence has a positive moderating effect on QM infrastructure practice and product innovation performance (H1a). When the market environment involves high level of uncertainty, a mature level of QM infrastructure practice leads to a positive improved level of product innovation performance. This is because, in such a market environment where customer preference and demands change quickly, firms need to take the initiative to analyze customers’ requirements and perform other market research, if they are to get ahead of their competition  . Driven by these factors, the creation of innovative products to meet customers’ expectations is more likely. The implication is that, in dynamic and turbulent market conditions, factors such as top management commitment, customer focus, and employee involvement would be extremely impactful on product innovation. These QM practices are not only useful in identifying customer needs and committing resources to support innovation work, but such QM practices can also inspire employees to recognize the importance of innovation and to enhance their level of commitment to the innovative functions and roles, in order to eventually improve the firm’s performance   . This conclusion is again in line with the work undertaken by Maria Leticia Santos-Vijande and Alvarez-Gonzalez (2007), and Z. Li, Su, and Song (2008) on a similar finding   .
Further, this study also shows that market turbulence has a negative moderating effect on the relationship between the core QM practices and process innovation (H2b). In other words, in a stabilized market, the core QM practices had a significant positive impact on innovation performance; however, when market turbulence is high, the impact of the core QM practice on innovation performance will decrease. A possible explanation for this is that when the market is characterized by a high level of certainty, the customer requirements and product preferences are predictable, as is the relationship with competitors. Firms tend to focus more on cost reduction, minimization of variation in production, and productivity improvement  . Meanwhile, process management following SOP would be able to reinforce management capability and support innovation better  . Besides, through collaboration with suppliers to acquire the needed technology and knowledge, it is likely that innovation will improve through continuous improvement  .
However, the results also reject the proposition regarding the positive moderating effect of market turbulence of QM infrastructure practices on process innovation performance. Possibly, this is because the high level of market turbulence demands radical technology development, and other radical changes; also, the firms will encounter various nonstructural demands. In order to cope with this, firms need to prioritize the product innovation strategy rather than that process innovation strategy with a limited amount of resources. Such a strategy can help firms to stay competitive and to respond quickly to customers’ changing requirements. As such, the moderating effect on QM infrastructure practices and process innovation performance is not sensitive to firms, and can be neglected. In addition, it might also be affected by the small number of firms sampled, as well as the bias implicit in the chosen provinces in which the firms operate; this could also cause such a moderate factor to become insignificant.
This study also failed to prove the proposition regarding the positive moderating effect of market turbulence of the core QM practices on process innovation performance (H3a). This is probably because, in a relatively stable market, firms are reluctant to introduce new products as a result of product innovation, as firms focus more on dealing with structured demands, meaning that process innovation matters more. Accordingly, product innovation performance does not improve much under such circumstances. So, the core QM practices regarding process innovation performance are not associated with the nature of turbulent market.
Finally, this study has several limitations. First of all, although this study has taken diversification into account, it might be more meaningful if more industries were included in the sample, and if the sample size could be improved. Secondly, this study adopts cross-sectional data for examination, but the disadvantage is that an effort needs to be given to identify the causal relationship between the variables  . If longitudinal research is adopted in future studies, the results might be more reliable. Last, given that the focus of QM research lies in the impact of contextual factors on QM practices, this study investigates the moderating effect of one of the key external factors―market turbulence―yet it is also necessary to take into account other internal factors. It is suggested that more thorough future research is needed to explore the effect of a combination of a set of moderating factors, be they external factors or internal factors, on the relationship between QM practices and innovation performance.
The fund projects, 1) Zhejiang Province philosophy and Social Sciences planning project, “The research on agricultural products quality assurance model based on niche theory” No. 13NDJC035YB; 2) Zhejiang province major humanities and social science research program, “Research on the mechanism of agricultural product quality security based on the mutual symbiotic relationship between the leading enterprises and farmers―from the perspective of l niche theory” No. 2013QN081; 3) The major project of the National Social Science Fund, “The research on quality management models and methods for Chinese major equipment products”, No. 12 & ZD206; 4) Soft science research projects of Zhejiang province, “Small and medium-sized enterprise social responsibility behavior model, incentive measures and countermeasures, based on the empirical study of Zhejiang province”, No. 2015C35050.
Cite this paper
Qunxiang Zhang,Xiaobin Feng,Xuan Xiang, (2016) The Impact of Quality Management Practices on Innovation in China: The Moderating Effects of Market Turbulence. American Journal of Industrial and Business Management,06,291-304. doi: 10.4236/ajibm.2016.63027