The moderating effect of average wage and number of stores on private label market share: A hierarchical linear model analysis
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Cited by 9 publications
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This research focuses on the analytic hierarchy model in the decision-making system that has a more complex structure and maintains the stability of the system, models the application process with the complexity and diversity of the rural economy, collects sample data with the help of different types of rural tourism questionnaire surveys, and integrates the data of rural tourism and other tourism into the model. The following are obtained: (1) During the level analysis, each phenotype track uses RRM, C 1 = 0.26 , C 2 = 0.223 , C 3 = 0.52 , C 4 = 0.25 , C 5 = 0.833 , C 6 = 0.442 , C 7 = 0.75 , C 8 = 0.127 , C 9 = 0.876 , C 10 = 0.792 , C 11 = 0.049 , C 12 = 0.16 , C 13 = 0.166 , and C 14 = 0.049 . The problems of the complex structure of the evaluation can be divided into simple analysis modules, and each module is analyzed at a level. The phenotypic trajectory of each individual is divided into target layer, standard layer, and scheme layer. (2) Arrangement and decision modeling were performed according to one or several indicators of different factors. In the hierarchical random regression model, APC = 0.214 , UPUA = 0.042 , TO = 0.081 , YPUA = 0.082 , PCP = 0.068 , and APS = 0.067 . The characteristic quantity analysis of different environments can be carried out, and the amplitude error and frequency error obtained are relatively small. IAND = 0.115 , AVA = 0.198 , RD = 0.119 , PI = 0.041 , PCCL = 0.142 , IOC = 0.201 , and DSTC = 0.069 . The comparison shows that the hierarchical analysis model is better than the hierarchical random regression model. (3) High-efficiency hybrid model correlation acceleration is the worst model. The experimental data are APC = 0.147 , UPUA = 0.029 , TO = 0.055 , YPUA = 0.06 , PCP = 0.047 , APS = 0.046 , IAND = 0.079 , AVA = 0.136 , RD = 0.082 , PI = 0.028 , PCCL = 0.098 , IOC = 0.139 , and DSTC = 0.048 . (4) The predicted 2020 data and the actual data have small errors. The data obtained by the AHP model is GDP = 1262.1 , finance = 185.09 , budget = 68 , tax = 51.92 , fund budget = 69.23 , transfer income = 40.14 , debt income = 7.73 , disposable financial power = 177.37 , fiscal expenditure = 191.26 , public budget = 88.68 , government expenditure = 71.39 , transfer expenditure = 23.46 , debt expenditure = 7.73 , and last year balance = 2.39 .
This research focuses on the analytic hierarchy model in the decision-making system that has a more complex structure and maintains the stability of the system, models the application process with the complexity and diversity of the rural economy, collects sample data with the help of different types of rural tourism questionnaire surveys, and integrates the data of rural tourism and other tourism into the model. The following are obtained: (1) During the level analysis, each phenotype track uses RRM, C 1 = 0.26 , C 2 = 0.223 , C 3 = 0.52 , C 4 = 0.25 , C 5 = 0.833 , C 6 = 0.442 , C 7 = 0.75 , C 8 = 0.127 , C 9 = 0.876 , C 10 = 0.792 , C 11 = 0.049 , C 12 = 0.16 , C 13 = 0.166 , and C 14 = 0.049 . The problems of the complex structure of the evaluation can be divided into simple analysis modules, and each module is analyzed at a level. The phenotypic trajectory of each individual is divided into target layer, standard layer, and scheme layer. (2) Arrangement and decision modeling were performed according to one or several indicators of different factors. In the hierarchical random regression model, APC = 0.214 , UPUA = 0.042 , TO = 0.081 , YPUA = 0.082 , PCP = 0.068 , and APS = 0.067 . The characteristic quantity analysis of different environments can be carried out, and the amplitude error and frequency error obtained are relatively small. IAND = 0.115 , AVA = 0.198 , RD = 0.119 , PI = 0.041 , PCCL = 0.142 , IOC = 0.201 , and DSTC = 0.069 . The comparison shows that the hierarchical analysis model is better than the hierarchical random regression model. (3) High-efficiency hybrid model correlation acceleration is the worst model. The experimental data are APC = 0.147 , UPUA = 0.029 , TO = 0.055 , YPUA = 0.06 , PCP = 0.047 , APS = 0.046 , IAND = 0.079 , AVA = 0.136 , RD = 0.082 , PI = 0.028 , PCCL = 0.098 , IOC = 0.139 , and DSTC = 0.048 . (4) The predicted 2020 data and the actual data have small errors. The data obtained by the AHP model is GDP = 1262.1 , finance = 185.09 , budget = 68 , tax = 51.92 , fund budget = 69.23 , transfer income = 40.14 , debt income = 7.73 , disposable financial power = 177.37 , fiscal expenditure = 191.26 , public budget = 88.68 , government expenditure = 71.39 , transfer expenditure = 23.46 , debt expenditure = 7.73 , and last year balance = 2.39 .
Rumination is a common problem and is associated with reduced psychological well-being. However, little is known about how rumination in the workplace is affected by interpersonal relationships. We propose that negative workplace behavior could serve as a potential influencing factor for rumination. Therefore, the current study constructed a multilevel moderated mediation model to investigate the relationship between workplace unit social undermining and interpersonal rumination. We also examined whether unit social support moderated that relationship and whether being the subject of envy mediated that relationship. Survey data were collected from 630 employees in China. The results indicate that: (1) a high level of unit social undermining by either a supervisor or co-workers has a significant positive influence on interpersonal rumination; (2) being the subject of envy exerts a mediating effect between unit supervisor social undermining and interpersonal rumination, as well as between unit co-worker social undermining and interpersonal rumination; and (3) unit social support moderates the associations between unit supervisor/co-worker social undermining and interpersonal rumination. These findings extend the research on rumination to the field of management and interpersonal relationships and emphasize the potential mechanisms of rumination, providing significant guidance for reducing staff rumination and improving psychological well-being.
Economy or premium? A systematic review of factors influencing retailers’ own product brand strategies
PurposeGrounded in strategic fit theory, this study aims to identify external and internal factors that influence retailers’ strategic choices regarding their own product brands. Furthermore, it seeks to explore the variations between different own product brand strategies in achieving both external and internal strategic fit.Design/methodology/approachThe systematic review method, incorporating a thematic analysis, was adopted, and 318 articles were included for review.FindingsThe factors that influence retailers’ strategic choices regarding their own product brands encompass a range of external macro and industrial environmental factors, along with various internal resource and capability factors. Moreover, the effects of these factors vary across different own product brand strategies.Originality/valueTo our knowledge, this is the first systematic review of research on retailers’ own product brands from a strategic management perspective, offering systematic and structured guidance for retailers.
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