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Drawing on cue utilization theory and the theory of motivated reasoning, we investigate the impact of consumer xenocentrism on product preferences in a situation where domestic products objectively outperform their foreign counterparts.

We develop and test a model linking xenocentrism to consumers’ preference towards domestic vs. foreign products by (1) examining the mediating role of consumers’ ability to identify the superior product and (2) assessing the role of product involvement in potentially moderating this relationship. An experimental design was employed, whereby respondents (Thai consumers, N = 579) were asked to compare two products in the same product category, one foreign and one domestic. In one condition, the foreign product outperformed the domestic one on a range of relevant product attributes, whereas in a second condition, the opposite was the case.

Our findings provide clear evidence that xenocentric consumers often cannot recognize the superiority of domestic products and, even if they do, they still exhibit preferences toward (inferior) foreign products. Thus, for xenocentric consumers, it seems that the country of origin (COO) overrides other cues and often results in suboptimal product choices.

Our study adds to our theoretical understanding of the functioning of the consumer xenocentrism construct and has important implications for foreign companies targeting xenocentric consumers as well as for local firms seeking to counteract xenocentric tendencies.

The attention of scholars and policy makers towards the topic of innovation has consistently increased, especially in recent years. This is justified by the fact that innovation undoubtedly plays, today, a crucial role in driving a country’s economic growth, improving productivity and, more generally, enhancing overall societal well-being.

When the discourse around innovation focuses on its economic dimension, the strong intertwinement with entrepreneurship emerges. In line with this, focusing on research on innovation in organisations and, especially, innovation in relation to the figure of the entrepreneur is considered, plenty of studies have been carried on, over time, in many disciplines, analysing the role of the entrepreneur in relation to innovation from various different angles. However, especially when management studies are considered, we can notice a poor consideration of the role played by the gender of the entrepreneur. In line with this consideration, by means of a systematic literature review, this chapter aims to fill this literature gap focusing on the intertwinement that can be envisaged, in management studies, among the issues of entrepreneurship and innovation in the case of women-owned firms.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

Although trust and distrust as distinct phenomena are of increasing interest to operations and supply chain management (OSCM) scholars, they have been inconsistently conceptualized and there is a lack of evidence regarding the distinctiveness of their respective antecedents. This study, therefore, focuses on one of the most widely accepted dimensions of trust, benevolence, to help more fully analyse (supplier) trust and distrust (in a buyer) and explore the effects of relational norms and structural power as specific antecedents.

The study employed a scenario-based role-playing experimental method. The proposed hypotheses were tested using structural equation modelling.

The results that while relational norms increase supplier trust, power asymmetry can simultaneously generate supplier distrust, support the coexistence of supplier trust and distrust in a buyer–supplier relationship.

This study is one of the first to explore the antecedents of supplier trust and distrust in a buyer. It demonstrates that supplier trust and distrust can coexist when the relationship is characterized by relational norms and asymmetrical power. This opens important questions for future trust–distrust research.

This paper aims to depart from the national innovation system (NIS) arguments that countries' institutional arrangements and performance result from various complementary factors that generate innovative activities and products within economies.

To further explore these dimensions, the main objective of this paper is to address the determinants of global heterogeneity in the innovation outcomes of the nations. Thus, the research employs descriptive data analysis and multivariate regression models, using data from the Global Innovation Index (GII) to analyze innovation systems cross-regionally concerning institutional arrangements and performance. Since 2013, the GII, has annually measured and ranked the innovation inputs and outputs of more than a hundred countries based on a comprehensive and sophisticated approach and a multidimensional perspective.

The author found the empirical results remarkably interesting in many respects. The different indexes of innovation inputs affect the country's performance level, but not all show a statistically significant impact on innovation outputs. Institutions and infrastructure indexes do not affect the innovative performance of the economies. The main determinants of innovation performance worldwide are business sophistication, human capital & research (HC&R) and market sophistication. In short, the research presents an original contribution, mainly because it explores different views on NIS disparities worldwide, using complementary methodological strategies and based on comprehensive data on innovative inputs and outputs in the countries.

The findings add new evidence-based knowledge on the determinants of innovation performance in different realities, such as political, economic and administrative. These realities formulate innovation policies and implement them worldwide.

The COVID-19 pandemic has caused havoc on a global scale for supply chains, which put forward higher demand for organizations to reassess their global supply chain strategy and improve supply chain sustainability. The purpose of this paper is to understand how leader's paradoxical cognition affect supply chain sustainability.

This study conceptualizes a research model grounded in upper echelons theory and propose a chain-mediating model under the moderating effect of big data analytics. Using PLS-SEM method, we test the hypotheses using survey data collected from supply chain managers or leaders of the supply chain team from 193 firms.

The results indicate that supply chain ambidexterity and organizational learning play a mediating role in the relationship between leaders' paradoxical cognition on supply chain sustainability, respectively, and these two variables have a chain-mediating role in the relationship above. In addition, the big data analytics negatively moderates the relationship between leader's paradoxical cognition and organizational learning, and further moderates our chain mediating model.

This research initiatively focuses on the micro-foundations of supply chain sustainability from managerial cognition and firstly provides empirical evidence about the impact of leader's paradoxical cognition on supply chain sustainability.

Organizational exploration has recently emerged in the literature as an essential aspect of contemporary organizations. However, little is known about its antecedents and, specifically, the role of contemporary leadership styles. The present study investigates the relationship between empowering leadership and organizational exploration. In addressing this relationship, we examine preoccupation with failure and leader gender as a mediator and a moderator, respectively.

We collected data from a sample of 326 US employees in March 2022. To test the present hypotheses, we used the PROCESS SPSS macro.

Our results supported our moderated mediation model, demonstrating that the indirect relationship of empowering leadership with organizational exploration via preoccupation with failure is stronger for male leaders than for female counterparts.

Concerning the practical implications of this study, organizations should be cognizant of empowering leaders’ impact on desirable outcomes. Additionally, organizations should promote preoccupation with failure to ameliorate organizational exploration. Preoccupation with failure can be achieved when error reporting is encouraged by organizations and supervisors and when a culture that promotes constructive feedback is established.

The present study offers novel insights into the underlying mechanisms and boundary conditions through which empowering leadership relates to organizational exploration. Additionally, it fills a gap in the literature concerning the relationship between empowering leadership and preoccupation with failure. Moreover, it adds to prior research regarding the outcomes of preoccupation with failure, filling the gap regarding the relationship between preoccupation with failure and organizational exploration. Lastly, it expands limited research focused on leader gender as a condition under which the effect of contemporary leadership styles could be enhanced or mitigated.

This study aims to investigate the behavior of the green biomass-derived copper (lignin/silica/fatty acids) complex, copper lignin/silica/fatty acids (Cu-LSF) complex, when incorporated into the nonpolar ethylene propylene diene (EPDFM) rubber matrix, focusing on its reinforcing and antioxidant effect on the resulting EPDM composites.

The structure of the prepared EPDM composites was confirmed by Fourier-transform infrared spectroscopy, and the dispersion of the additive fillers and antioxidants in the EPDM matrix was investigated using scanning electron microscopy. Also, the rheometric characteristics, mechanical properties, swelling behavior and thermal gravimetric analysis of all the prepared EPDM composites were explored as well.

Results revealed that the Cu-LSF complex dispersed well in the nonpolar EPDM rubber matrix, in thepresence of coupling system, with enhanced Cu-LSF-rubber interactions and increased cross-linking density, which reflected on the improved rheological and mechanical properties of the resulting EPDM composites. From the various investigations performed in the current study, the authors can suggest 7–11 phr is the optimal effective concentration of Cu-LSF complex loading. Interestingly, EPDM composites containing Cu-LSF complex showed better antiaging performance, thermal stability and fluid resistance, when compared with those containing the commercial antioxidants (2,2,4-trimethyl-1,2-dihydroquinoline and N-isopropyl-N’-phenyl-p-phenylenediamine). These findings are in good agreement with our previous study on polar nitrile butadiene rubber.

The current study suggests the green biomass-derived Cu-LSF complex to be a promising low-cost and environmentally safe alternative filler and antioxidant to the hazardous commercial ones.