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Based on the socio-emotional wealth and agency theories, this study empirically investigates the impact of family ownership and management on green innovation (GI) in family businesses, as well as the moderating effects of institutional environmental support factors, namely, the technological achievement marketisation index and the market-rule-of law index.

This study empirically tests the hypotheses based on a sample of listed Chinese family companies with A-shares in 14 heavily polluting industries from 2009 to 2019.

There is a U-shaped relationship between the percentage of family ownership and GI, and an inverted U-shaped relationship between the degree of family management and GI. Additionally, different institutional environmental support factors affect these relationships in different ways. As the technological achievement marketisation index increases, the U-shaped relationship between the percentage of family ownership and GI becomes steeper, while the inverted U-shaped relationship between the degree of family management and GI becomes smoother. The market rule-of-law index weakens the U-shaped relationship between family ownership and GI.

First, the authors enrich the research on the driving factors of GI from the perspective of the most essential heterogeneity of family businesses. This study shows nonlinear and opposite effects of family ownership and management on GI in family firms. Second, this study contributes to the literature on family firm innovation. GI, not considered by researchers, is regarded as an important deficiency in research on innovation in family businesses. Therefore, this study fills that gap. Third, the study expands research on moderating effects in the literature on GI from the perspective of institutional environmental support factors.

The existing literature documents mixed evidence toward the association between corporate social responsibility (CSR) and corporate tax planning (e.g., Davis, Guenther, Krull, & Williams, 2016; Hoi, Wu, & Zhang, 2013). In this study, I aim to identify a causal relationship between CSR and tax planning, leveraging the staggered adoptions of constituency statutes in US states, which is a plausibly exogenous shock to firms' emphasis on their social responsibility. In general, the statutes permit firm directors to consider the interests of all constituents when making business decisions, including those who benefit from firms paying their fair share of income taxes. Thus, the adoption of the statutes raises the importance of firms' social responsibility in paying income taxes. Employing a staggered difference-in-differences (DiD) method, I find that firms incorporated in states that have adopted constituency statutes exhibit significantly higher effective tax rates (ETRs) based on current tax expense. This causal relationship suggests that managers, with the legitimacy to consider the social impact of tax avoidance, become less aggressive in tax planning. I further find that the effect of adoption is stronger for financially unconstrained firms and firms in retail businesses, where the demand (cost) for tax avoidance is lower (higher). Finally, I show that my main results are driven by firms located in states with a high sense of social responsibility and firms with high levels of tax avoidance prior to the adoption. Overall, the findings in this chapter contribute to the literature by delineating a negative causal relationship between CSR and tax avoidance and identifying a positive social impact brought by the passage of constituency legislation.

This study aims to focus on the clothing recycling supply chain and aims to provide optimal decisions and managerial insights into supply chain strategies, thereby facilitating the sustainable development of the clothing industry.

Based on previous single- and dual-channel studies, game theory was employed to analyze multiple recycling channels. Concurrently, clothing consumer types were integrated into the analytical models to observe their impact on supply chain strategies. Three market scenarios were modeled for comparative analysis, and numerical experiments were conducted.

The intervention of fashion retailers in the clothing recycling market has intensified competition across the entire market. The proportions of various consumer types, their preferences for online platforms and their preference for the retailer’s channel influence the optimal decisions and profits of supply chain members. The diversity of recycling channels may enhance the recycling volume of clothes; however, it should meet certain conditions.

This study extends the existing theory from a channel dimension by exploring multiple channels. Furthermore, by investigating the classifications of clothing consumers and their influence on supply chain strategies, the theory is enhanced from the consumer perspective.

Fused deposition modeling (FDM) is an extensively used additive manufacturing method with the capacity to build complex functional components. Due to the machinery and environmental factors during manufacturing, the FDM parts inevitably demonstrated uncertainty in properties and performance. This study aims to identify the stochastic constitutive behaviors of FDM-fabricated polylactic acid (PLA) tensile specimens induced by the manufacturing process.

By conducting the tensile test, the effects of the printing machine selection and three major manufacturing parameters (i.e., printing speed S, nozzle temperature T and layer thickness t) on the stochastic constitutive behaviors were investigated. The influence of the loading rate was also explained. In addition, the data-driven models were established to quantify and optimize the uncertain mechanical behaviors of FDM-based tensile specimens under various printing parameters.

As indicated by the results, the uncertain behaviors of the stiffness and strength of the PLA tensile specimens were dominated by the printing speed and nozzle temperature, respectively. The manufacturing-induced stochastic constitutive behaviors could be accurately captured by the developed data-driven model with the R² over 0.98 on the testing dataset. The optimal parameters obtained from the data-driven framework were T = 231.3595 °C, S = 40.3179 mm/min and t = 0.2343 mm, which were in good agreement with the experiments.

The developed data-driven models can also be integrated into the design and characterization of parts fabricated by extrusion and other additive manufacturing technologies.

Stochastic behaviors of additively manufactured products were revealed by considering extensive manufacturing factors. The data-driven models were proposed to facilitate the description and optimization of the FDM products and control their quality.

The main purpose of this study is to analyze how consumers’ visual attention to wine label design correlates with their preferences. Accordingly, this study uses quantitative eye-tracking metrics to understand which design proposal has greater visual salience. A more specific objective was to assess which design proposal was preferred to be marketed.

The experiment involved evaluating of three different labeling proposals of an Italian winery. Infrared eye-tracking was used to measure implicit eye movements on the three bottles displayed, simultaneously, on a computer screen. A generalized linear model was used to test how consumers' visual attention to wine label design correlated with their preferences.

The design proposals were evaluated significantly differently, with one set being preferred. In general, a strong positive relationship was found between pausing to peruse a specific design proposal and making an explicit choice of the same bottle.

The main limitation of the experiment concerns the sample interviewed. As the sample is homogeneous, the results may not be generalizable to other segments. Furthermore, the addition of electroencephalographic devices that monitor brain activity could provide crucial information for understanding consumer behavior during the purchase decision-making process.

Eye-tracking methods could be useful for designers and wine producers during the evaluation process of design projects.

The use of eye-tracking for evaluating design proposals before placing a product on the market is relatively novel. This method provides objective, quantitative and predictive information on consumer preferences contributing guidelines to designers and marketers during the product conception phase.

This study aims to reach a perception of the advance of research on deviant organisational behaviours.

This research has been done through the text mining method. By reviewing, the papers were selected 360 papers between 1984 and 2020. Based on the Davis–Boldin index, 11 optimal clusters were gained. Then the roots were ranked in any group, using the Simple Additive Weighting technique. Data were analysed by RapidMiner and MATLAB software.

According to the results obtained, clusters are included leadership styles, job attitudes, spirituality in the workplace, work psychology, personality characteristics, classification and management of deviant workplace behaviours, service and customer orientation, deviation in sales, psychological contracts, group dynamics and inappropriate supervision.

This study provides a landscape and roadmap for future investigation on deviant organisational behaviours.