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The purpose of this study to improve the efficiency of customer acquisition and retention through the design of salary information disclosure mechanism.

This study develops a stylized game-theoretic model of delegating customer acquisition and retention, focusing on how firms choose delegation and wage information disclosure strategy.

The results confirm the necessity for enterprises to disclose salary information. When sales agents are risk neutral, firms should choose multi-agent (MA) delegation and disclose their wages. However, when agents are risk averse, firms may disclose the wages of acquisition agents or both agents in MA delegation, depending on the uncertainty of the retention market.

This paper contributes to the literature on delegation of customer acquisition and retention and demonstrates that salary disclosure can be used as a supplement to the incentive mechanism.

This paper aims to offer the first known synthesis of peer-reviewed literature on trade-based money laundering (TBML). Given the topic is in its nascent stage yet gaining prominence across scholarship and practice, this foundation is pertinent for future TBML research.

A systematic literature review was undertaken with a formulaic search string. Both qualitative (thematic) and quantitative (meta) analysis methods were used to illustrate the findings.

The systematic literature review, using qualitative and quantitative synthesis, led to a thematic categorization of extant TBML literature into four categories: TBML risk assessment, TBML detection, the role of professionals and understanding of TBML. Due to the limited number of studies, insights that can be drawn from the extant literature on the best way to combat TBML are also limited.

As the first systematic literature review on TBML, this study identified that the existing TBML literature has focused on increasing the understanding of the phenomenon in terms of its definition and mechanisms, detection, linkage with other crimes, such as organized crime and terrorism financing, and risk assessment frameworks. The originality of these findings lies in identifying areas future researchers might explore to broaden the academic literature.

Aqueous zinc-ion battery has broad application prospects in smart grid energy storage, power tools and other fields. Co₃O₄ is one of the ideal cathode materials for water zinc-ion batteries due to their high theoretical capacity, simple synthesis, low cost and environmental friendliness. Many studies were concentrated on the synthesis, design and doping of cathodes, but the effect of process parameters on morphology and performance was rarely reported.

Herein, Co₃O₄ cathode material based on carbon cloth (Co₃O₄/CC) was prepared by different temperatures hydrothermal synthesis method. The temperatures of hydrothermal reaction are 100°C, 120°C, 130°C and 140°C, respectively. The influence of temperatures on the microstructures of the cathodes and electrochemical performance of zinc ion batteries were investigated by X-ray diffraction analysis, scanning electron microscopy, cyclic voltammetry curve, electrochemical charging and discharging behavior and electrochemical impedance spectroscopy test.

The results show that the Co₃O₄/CC material synthesized at 120°C has good performance. Co₃O₄/CC nanowire has a uniform distribution, regular surface and small size on carbon cloth. The zinc-ion battery has excellent rate performance and low reaction resistance. In the voltage range of 0.01–2.2 V, when the current density is 1 A/g, the specific capacity of the battery is 108.2 mAh/g for the first discharge and the specific capacity of the battery is 142.6 mAh/g after 60 charge and discharge cycles.

The study aims to investigate the effect of process parameters on the performance of zinc-ion batteries systematically and optimized applicable reaction temperature.

The current study aims to investigate the impact of perceived blockchain-related information transparency on consumers’ intention to purchase organic food. This study examines how perceived blockchain- related information transparency, directly and indirectly, affects purchase intentions through attitudes, perceived behavioural control and subjective norms. Additionally, the study explores how blockchain-based trust moderates the influence of perceived blockchain-related information transparency on these factors and the intention to purchase organic food.

Based on the theory of planned behaviour framework and a sample of 5,326 consumers, this study uses partial least squares structural equation modelling to test the research model.

This study finds that perceived blockchain-related information transparency directly enhances consumers’ attitudes towards organic food purchase, perceived behavioural control, subjective norms and intention to purchase organic food. Additionally, perceived blockchain-related information transparency indirectly affects consumers’ intention to buy organic food through three antecedents of the theory of planned behaviour model. Notably, these indirect effects were moderated by consumers’ blockchain-based trust.

This study provides recommendations for leveraging blockchain to enhance transparency and build trust, which could boost consumer engagement and organic food purchases.

This research contributes to blockchain literature by empirically examining the role of perceived blockchain-related transparency and blockchain-based trust in consumers’ purchasing decisions regarding organic food. It provides valuable insights into the consumer-centric benefits of blockchain technology. Furthermore, this study also contributes to the literature on organic food, particularly its promotion through blockchain technology.

This study explores features that impact consumers' purchase intention through artificial intelligence (AI), because it is believed that through artificial intelligence, consumers' intention to purchase grows significantly, especially in the retail sector, whereby retailers provide lucrative offers to motivate consumers. The study develops a theoretical framework based on media-richness theory to investigate the role of perceived anthropomorphism toward an intention to purchase products using AI.

The study is based on cross-sectional data through an online survey. The data have been analyzed using PLS-SEM and SPSS PROCESS macro.

The results show that consumers tend to demand anthropomorphized products to gain a better shopping experience and, therefore, demand features that attract and motivate them to purchase through artificial intelligence via mediating variables, such as perceived animacy and perceived intelligence. Moreover, trust in artificial intelligence moderates the relationship between perceived anthropomorphism and perceived animacy.

The study investigates and concludes with managerial and academic insights into consumer purchase intention through artificial intelligence in the retail and marketing sector.

The application of steel fiber reinforced concrete (SFRC) beams is limited in practice, partially due to the lack of accurate shear strength prediction models. This study aims to develop a reliable shear strength prediction model for SFRC beams.

In this study, an artificial neural network was employed to predict the shear strength of SFRC beams, utilizing a comprehensive database of 562 experimental studies. Multiple neural networks were established with varying hyperparameters, and their performance was evaluated using statistical parameters.

The neural network with 11 neurons showed superior results than other networks. The performance evaluation, efficiency and accuracy of the selected neural network were examined using margin of deviation, k-fold cross-validation, Shapley analysis, sensitivity analysis and parametric analysis. The proposed artificial neural network model accurately predicts the shear strength and outperforms other existing equations.

This research contributes to overcoming the limitations of existing prediction models for shear strength of SFRC beams without stirrups by developing a highly accurate model based on ANN. Utilizing a comprehensive database and rigorous evaluation techniques enhances the reliability and applicability of the proposed model in practical engineering applications.

This study introduces a comprehensive bibliometric analysis of the foreign direct investment (FDI) literature by multinational corporations (MNCs) focusing on emerging economies to identify the most influential authors, journals and articles in FDI research and reveals the fields' conceptual and intellectual structures. The purpose of this paper is to address these issues.

The study analyzed 533 articles published between 1974 and 2020 in 226 academic journals indexed in the Web of Science (WoS) and Scopus databases. We used the R language for statistical computing to map author collaboration, co-word and develop a conceptual and intellectual map of the field.

The results show that, although the FDI literature has many authors, few dominate the field. The International Business Review (IBR) and International Journal of Emerging Markets (IJoEM) are the main sources of the publications. Moreover, bibliometric laws show that our dataset follows the Lotka law of scientific productivity and Bradford law of scattering, identifying the core journals. Finally, FDI by MNCs in emerging economies research is divided into four sub-research themes related to (1) FDI determinants, (2) entry mode, (3) MNCs and FDI performance and (4) the internationalization process.

The current article provides several starting points for practitioners and researchers investigating FDI. It contributes to broadening the vision of the field and offers recommendations for future studies.

A growing number of organizational scandals, including Apple slowing old devices to increase the sales of new ones, and research on unethical pro-organization behaviors (UPBs) have heightened the need to explore the phenomenon. Extending the current understanding, the study's major purpose is to investigate individual-level factors that may shape their willingness to engage in UPBs. It also inquires whether moral disengagement processes influence this association.

After testing the reliability and validity of the latent constructs and ensuring common method bias did not contaminate the data, the study used the PLS-SEM approach to analyze the primary data collected from 408 full-time Pakistani employees.

Results add to the current understanding by revealing that individual-level dark factor Machiavellianism (MACH) significantly influences employees' willingness to engage in UPBs. Accordingly, affective commitment is another individual-level factor that encourages employees to be a part of UPBs. Lastly, results unveil that employees with a higher moral disengagement are more prone to engage in UPBs.

The study measured employees' willingness or intentions to engage in UPBs, not their actual involvement.

Results clarify to the top management that individuals high on MACH, affective commitment and moral disengagement are more prone to be involved in UPBs.

This study is among the preliminary ones that assess the direct associations between MACH, affective commitment, and UPBs, especially in the Pakistani context. Moreover, exploring the moderating role of moral disengagement between the above associations is also an under-researched phenomenon.