Scholars have paid so much academic attention to UPB in the past decade. However, there is lacking a quantitative review to uncover the relationship between UPB and its antecedents. To address this, we make a meta-analytic review about UPB. Specifically, we propose a theoretical framework of antecedents of UPB and test it using meta-analysis technology (k = 67, n = 20,957). We found moral disengagement, organizational identification, identification with supervisors, leader UPB, ethical judgments, psychological entitlement, transformational leadership, and job satisfaction (...) are positively related to UPB. Moral identity and taking responsibility are negatively related to UPB. Besides, ethical leadership, abusive supervision, and authoritarian leadership have double-edged sword effects on UPB. For ethical leadership, it increases UPB via LMX, on the other hand, it decreases UPB via moral disengagement. For abusive supervision and authoritarian leadership, they increase UPB via moral disengagement and decrease UPB via LMX meanwhile. We discuss the results of the current meta-analysis and put some future research directions. (shrink)

This study conducted a social network analysis of the evolutionary characteristics of the world dairy trade network based on the overall trade pattern. In addition, the evolution of trade blocs and the co-opetition relationships involving dairy products in major countries were analyzed in terms of supply and demand. The results show that continuous and complex changes have taken place in the world’s dairy trade network since 2001. The number of trade entities in dairy products has stabilized since 2012. At present, (...) approximately 94% of countries (regions) are involved in dairy product trade, such that the world dairy trade network exhibits the small-world effect and scale-free property. The world import pattern for dairy products has changed. While export centers have not changed, import centers have shifted from Europe, America, and East Asia to North America, East Asia, and the Middle East. The world dairy trade network consists of the EU trade bloc headed by Germany, the former Soviet Union–Brazil trade bloc, and the Asia–Australia–America trade bloc. The trade blocs have evolved due to geographical positions, historical cultures, and political relations. In a trade bloc, the diversification of import sources is more prominent in demand countries. European and Asian markets have become the main markets of the major exporters. In this study, the evolutionary characteristics of the world dairy trade network and the co-opetition relationships were analyzed to provide scientific support to inform the development of dairy trade policies. The results can provide technical and psychological support to policy-makers in various countries in their dairy trade decision-making. (shrink)

Evaluating the price policy of raw milk is of great significance to the sustainable development of an industry supply chain. In this context, our study used the multi-period difference-in-difference method to systematically examine the impact of the policy implementation on product price and profit distribution in the supply chain. The results showed the following: the price of raw milk in the implementation area of the price support policy is 13.54% higher than that of the unimplemented area; the effect of price (...) increase in the western region is higher than that in the eastern region, and the central region ; furthermore, the purchase price guidance policy of raw milk drives price increase or price suppression in the links of the supply chain to promote a balanced distribution of profits among the participants in the chain. These conclusions all have good stability and have reference significance for further improving and adjusting the price support policy of raw milk to realize the sustainable development of the Chinese dairy industry. This will enhance the production confidence of Chinese raw milk producers and improve Chinese consumers’ expectations and consumer psychology regarding domestic dairy products. (shrink)

Product innovation integrates technology, knowledge, management practices, and market innovation, making it essential to gain a competitive advantage. Effective management of dynamic knowledge, which is the foundation of and driving force for product innovation, is a powerful tool that allows a firm to successfully innovate, adapt to environmental changes, and improve its competitiveness. In the “nanosecond age,” unlearning and learning in an organization is crucial to a firm’s ability to promptly update its organizational knowledge and maintain innovation vitality. Based on (...) the dynamic knowledge management perspective, this study integrates and constructs a theoretical model with environmental dynamism as the moderating variable, discusses the impact of organizational unlearning on product innovation performance, and empirically analyzes 208 valid questionnaires in the Yangtze River Delta using the multiple regression method. The results show that organizational unlearning shares a positive relationship with dynamic capabilities and product innovation performance. Dynamic capability is positively related to product innovation performance and has a partial mediating effect on the relationship between organizational unlearning and product innovation performance. Environmental dynamism shares a positive moderating effect on the relationship between organizational unlearning and product innovation performance. This study deepens the existing research on the factors that influence product innovation performance, which may help firms improve their dynamic knowledge management and product innovation performance. (shrink)

Traditional multiple-group confirmatory factor analysis is usually criticized for having too restrictive model assumption, namely the scalar measurement invariance. The new multiple-group analysis methodology, alignment, has become an effective alternative. The alignment evaluates measurement invariance and more importantly, permits factor mean comparisons without requiring scalar invariance which is usually required in traditional multiple-group CFA. Some simulation studies and empirical studies have investigated the applicability of alignment under different conditions, but some areas remain unexplored. Based on the simulation studies of Asparouhov (...) and Muthén and of Flake and McCoach, this current simulation study is broken into two sections. The first study investigates the minimal group sizes required for alignment in three-factor models. The second study compares the performance of multiple-group CFA, multiple-group exploratory structural equation model, and alignment by including different proportions and magnitudes of cross-loadings in the models. Study 1 shows that when the model has no noninvariant parameters, the alignment requires relatively lower group sizes. Explicitly, the minimal group size required for alignment was 250 when the amount of groups was three, the minimal group size was 150 when the amount of groups was nine, and 200 when the amount of groups was 15. When there are noninvariant parameters in the model and the amount of groups is low, a group size of 350 is a safe rule of thumb. When there are noninvariant parameters in the model and the amount of groups is high, a group size of 250 is required for trustworthy results. The magnitude of noninvariance and the noninvariance rate do not affect the minimal group size required for alignment. Study 2 shows that multiple-group CFA provides accurate factor mean estimates when each factor had 20% factor loading with small-sized cross-loading. Multiple-group ESEM provides accurate factor mean estimates when the magnitude of cross-loading is small or when each factor had 20% factor loading with medium-sized cross-loading. Alignment provides accurate factor mean estimates when there are only small-sized cross-loadings in the model. The parameter estimates, coverage rates and ratios of average standard error to standard deviation for each methodology are not influenced by the amount of groups. Recommendations are concluded for using multiple-group CFA, multiple-group ESEM, traditional alignment and aligned ESEM based on the results. Multiple-group CFA is more suitable for use when scalar invariance is established. Multiple-group ESEM works best when there are small-sized or only a few medium-sized cross-loadings in the model. Traditional alignment allows for small-sized cross-loadings and a few noninvariant parameters in the model. AESEM integrates the advantages of alignment and ESEM, can provide accurate estimates when noninvariant parameters and cross-loadings both exist in the model. Compared to multiple-group CFA, multiple-group ESEM, the alignment methodology performs well in more situations. (shrink)