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This study aims to take a dual-process perspective and argues that peer influence on increasing impulse buying may also operate automatically. If-then plans, which can automate action control, may, thus, help regulate peer influence. This research extends existing literature explicating the deliberate influence of social norms.

Study 1 (N = 120) obtained causal evidence that forming an implementation intention (i.e. an if-then plan designed to automate action control) reduces peer impact on impulse buying in a laboratory experiment with young adults (students) selecting food items. Study 2 (N = 686) obtained correlational evidence for the role of norms, automaticity and implementation intentions in impulse buying using a large sample of high-school adolescents working on a vignette about clothes-shopping.

If-then plans reduced impulse purchases in the laboratory (Study 1). Both reported deliberation on peer norms and the reported automaticity of shopping with peers predicted impulse buying but an implementation intention to be thriftily reduced these links (Study 2).

This research highlights the role of automatic social processes in problematic consumer behaviour. Promising field studies and neuropsychological experiments are discussed.

Young consumers can gain control over automatic peer influence by using if-then plans, thereby reducing impulse buying.

This research helps understand new precursors of impulse buying in understudied European samples of young consumers.

Rapid sensitivity analysis and near-optimal decision-making in contested environments are valuable requirements when providing military logistics support. Port of debarkation denial motivates maneuver from strategic operational locations, further complicating logistics support. Simulations enable rapid concept design, experiment and testing that meet these complicated logistic support demands. However, simulation model analyses are time consuming as output data complexity grows with simulation input. This paper proposes a methodology that leverages the benefits of simulation-based insight and the computational speed of approximate dynamic programming (ADP).

This paper describes a simulated contested logistics environment and demonstrates how output data informs the parameters required for the ADP dialect of reinforcement learning (aka Q-learning). Q-learning output includes a near-optimal policy that prescribes decisions for each state modeled in the simulation. This paper's methods conform to DoD simulation modeling practices complemented with AI-enabled decision-making.

This study demonstrates simulation output data as a means of state–space reduction to mitigate the curse of dimensionality. Furthermore, massive amounts of simulation output data become unwieldy. This work demonstrates how Q-learning parameters reflect simulation inputs so that simulation model behavior can compare to near-optimal policies.

Fast computation is attractive for sensitivity analysis while divorcing evaluation from scenario-based limitations. The United States military is eager to embrace emerging AI analytic techniques to inform decision-making but is hesitant to abandon simulation modeling. This paper proposes Q-learning as an aid to overcome cognitive limitations in a way that satisfies the desire to wield AI-enabled decision-making combined with modeling and simulation.

This study aims to extend the literature on psychological contracts, employee mental health, self-control and equity sensitivity among employees in Ghana.

Data for this study came from a sample of 484 employees from an organisation in the telecommunication sector of Ghana. The details of the study were discussed with employees after which they were given the choice to participate in the study.

The present study found that psychological contract breach is directly associated with mental health and indirectly related to mental health through equity sensitivity and self-control.

The findings suggest that psychological contracts are important aspects of the employment relationship that could be used to enhance employee mental health. Furthermore, enhancing employees’ self-control and resolving issues of individuals high on equity sensitivity are effective ways that organisations can deploy to sustain mental health in the face of psychological contract breaches.

This study aims to examine whether chief executive officer (CEO) pay-performance sensitivity to shareholder wealth is related to the use of non-financial performance measures in incentive contracts.

Using hand-collected performance measure data in a sample of S&P 500 firms across the period 1994–2010, this study investigates the sensitivity of CEO bonus and cash pay to shareholder wealth of firms that use non-financial performance (NFPM) measures of varying types and contractual weights in their bonus contracts along with financial measures (NFPM firms) in comparison to that of firms using financial measures only (FPM firms).

This study finds evidence that the pay-performance sensitivity is stronger in NFPM firms than in FPM firms. These results are driven by the use of CEO individual goals and operational efficiency. Furthermore, when using environmental, social and governance factors, the pay-performance sensitivity is stronger in terms of accounting performance only. This study also finds that using NFPM enhances pay-performance sensitivity more as their contractual weights increase and as financial risk increases.

These findings are important to stakeholders, and especially regulators in understanding incentive effects of alternative performance measures. This study also sheds light on what types of non-financial measures are better in helping firms align CEOs’ incentives to shareholders’ interests.

This study contributes to prior research on benefits of non-financial information within the context of executive compensation. This study presents original results about the effects of contractual weights of non-financial measures and financial risk on CEO pay-performance sensitivity. This study also presents new insights regarding how different types of non-financial measures affect CEO pay-performance sensitivity.

From the quantum game perspective, this paper aims to study a green product optimal pricing problem of the dual-channel supply chain under the cooperation of the retailer and manufacturer to reduce carbon emissions.

The decentralized and centralized decision-making optimal prices and profits are obtained by establishing the classical and quantum game models. Then the classical game and quantum game are compared.

When the quantum entanglement is greater than 0, the selling prices of the quantum model are higher than the classical model. Through theoretical research and numerical analysis results, centralized decision-making is more economical and efficient than decentralized decision-making. Publicity and education on carbon emission reduction for consumers will help consumers accept carbon emission reduction products with slightly higher prices. When the emission reduction increases too fast, the cost of emission reduction will form a significant burden and affect the profits of manufacturers and supply chain systems.

From the perspective of the quantum game, the author explores the optimal prices of green product and compares them with the classical game.

The purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.

The design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.

The findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.

The paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

Information-facilitated product recovery system (IFPRS) has captivated industry attention and has developed into a matter of consideration among the researchers because of enhanced climate concerns, jurisdictive logics and societal liabilities. Although IFPRS implementation has become an essential aspect in manufacturing industries functional in the developed nations, still, limited consideration has been given in the literature to analyze the issues to IFPRS implementation for a circular economy (CE) in emerging and developing nations. Therefore, the objective of this study is to recognize issues to implementing IFPRS for a CE in context of select manufacturing industries in India.

In this study, 24 potential issues are established from the literature and from suggestions from the experts. The issues are clubbed under five different perspectives of technical, government, organization, policy and knowledge. Further, fuzzy VIKOR technique is applied on the results obtained to prioritize the identified issues. A sensitivity analysis has been carried out to check the robustness of the framework.

The present study shows that lack of skills and expertise in IFPRS implementation for a CE (I2), deficient capital to implement a CE in IFPRS (I₉), inadequate in adopting recent IT technology (I₁₈), feasibility of IFPRS employment for a CE (I₆) and no efficient training and program to CE adoption (I₂₁) are the top five potential issues in implementing IFPRS practices for a CE in Indian manufacturing industries.

In literature, limited study has been observed on determining issues to implementation of IFPRS for a CE. A more systematic method and statistical confirmation is necessary to establish further new confronting issues. This study is limited to Indian manufacturing industries.

The main contribution of this study includes identification of issues and later prioritizing them to reflect their severity. This would help the industry practitioners to formulate strategies for handling the issues conveniently.

This paper aims to explore how and why salespeople enhance or hinder long-term business-to-business (B2B) customer relationships at the interpersonal level by considering self-disclosure and relational cost and reward evaluations.

Data from interviews (N = 47) with B2B sales professionals were analyzed, focusing on the shift of the phases in long-term B2B customer relationships.

Long-term B2B customer relationships evolve at the interpersonal level through a process of continuous relational cost and reward evaluation, self-disclosure and business disclosure in three phases: becoming business partners, collaborative partners and collaborative and personal partners. The reward evaluations progress from being business related to including even more relational benefits. Disclosure progresses through general business disclosure and general self-disclosure; strategic business disclosure and personal life self-disclosure; and synergistic business disclosure and private self-disclosure.

The long-term B2B customer relationships could be studied at the interpersonal level from the customer’s perspective. Self-disclosure could be studied in cross-cultural settings as well as gender differences should be considered in future studies. Business and social penetration theory could be applied to investigate different types of relationships and other professional relationships, such as those between employers and employees. It would be important to test whether the business-related and self-disclosure subtypes apply to the development of other types of professional relationships or whether other disclosure subtypes exist. The authors recommend exploring salespeople’s and customers’ privacy management strategies in multiple communication channels.

Managers may apply the results of this study in their customer relationship management and sales training.

The findings outline a contextual extension of social penetration theory.

Teamwork has long featured in social science research. Further, with research increasingly “cross-national,” communication becomes more complex, for instance, involving different cultures, languages and modes of communication. Yet, studies examining team communicative processes that can facilitate or constrain collaboration are rare. As a cross-national European team representing varied disciplines, experiences, languages and ethnicities, we undertook to examine our communication processes with the aim to promote better qualitative research practices.

Viewing reflection as a tool for enhancing workplace practices, we undertook a structured reflection. We developed an empirically derived framework about team communication, then used it to analyse our interaction practices and their relative effectiveness.

The results highlighted two under-examined influences, the use of different modes of communication for different purposes and the need for face-to-face communication to address a particularly challenging aspect of research, negotiating a shared coding scheme to analyse diverse cultural and linguistic qualitative data.

The study offers a procedure and concepts that others could use to examine their team communication.

The communicative processes that can constrain and facilitate effective cross-national research team collaboration are rarely examined. The results emphasise the need for careful negotiations around language, epistemologies, cultures and goals from the moment collaboration begins in formulating a project, through applying for grant funds, to when the last paper is published – timely in a context in which such work is increasingly expected.