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Distributed photovoltaic (DPV) projects generally have output risks, and the production effort of the supplier is often private information, so the buyer needs to design the optimal procurement contract to maximise its procurement utility.

Based on the principal-agent theory, we design optimal procurement contracts for DPV projects with fixed payments and incentive factors under three situations, i.e. symmetry information, asymmetry information without monitoring and asymmetry information with monitoring. We obtain the optimal production effort and expected utility of the supplier, the expected output and expected utility of the buyer and analyse the value of the information and monitoring.

The results show that under asymmetric information without monitoring, risk-averse suppliers need to take some risk due to output risk, which reduces the optimal production effort of the supplier and the expected output and expected utility of the buyer. Therefore, when the monitoring cost is below a certain threshold value, the buyer can introduce a procurement contract with monitoring to address the asymmetry information. In addition, under asymmetric information without monitoring, the buyer should choose a supplier with a low-risk aversion.

Considering the output risk of DPV projects, we study the optimal procurement contract design for the buyer under asymmetric information. The results provide some theoretical basis and management insights for the buyer to design optimal procurement contracts in different situations.

There is growing scholarly interest in the use of penalty in employment contracts which reduce employees' pay if the employee's performance does not meet a pre-specified performance threshold. Prior accounting research has focused exclusively on the effect of penalty on employee performance. In this study, the authors extend earlier research by examining how penalty affects the employers' wage offers. Prior research suggests that employers' generous wage offers in employment contracts are normally translated as trust by employees who in turn reciprocate with higher effort. The authors present a theory that predicts penalty reduces employers' wage offers. Then, the authors propose unrestricted communication between employers and employees as a potential moderator for the negative effect of penalty on trust and reciprocity.

The authors implement a controlled lab experiment with a 2 × 3 experimental design (Penalty: Present and Absent; and Communication: None, One-Way and Two-Way).

The authors develop their predictions by utilizing insights from motivational-crowding and organizational communication theories. The authors hypothesize and find evidence that employers' ability to penalize employees can reduce employers' motivation to offer generous wages. As a result, reduced trust demotivates employees to provide high effort. However, the authors find that a two-way communication moderates the negative effect of penalties by restoring trust, thereby, increasing reciprocity. Finally, the authors find evidence that relationship-oriented messages explain the moderating effect of communication.

This study is subject to limitations inherent in all experimental studies. The decisions in the study experiment are less complex than those found in practice. Moreover, there are significantly higher costs and potential benefits to shirk on effort in practice. The authors encourage future research on other organizational features that would influence the generalizability of their theory and results. Nonetheless, this study makes an important contribution to the literature on trust, reciprocity, gift-exchange contracts, managerial controls and communication.

This paper has several important implications for theory and practice. The authors show that the presence of penalty may not automatically result in increasing employees' effort level, contrary to traditional economic theory predictions. This effect is driven mainly by the crowding out effect of a penalty on employers' desire to signal trust. Therefore, the presence of an open communication channel may become an important tool to reverse the psychological effect of reduced trust when penalty is present. Therefore, the study's findings contribute to the trust–reciprocity literature on how management control system influences employers' and employees' behavior. These findings are especially germane given the trend in the workplace toward establishing open communication at different levels within the firm hierarchy. The study also contributes to the literature on trust–reciprocity as critical informal controls and social norms in accounting practices (Bicchieri, 2006; Stevens, 2019), shedding light on how firms may influence employees' reciprocity in management control practices and induce them to act in line with the firm's objectives by opening communication channels.

Prior accounting research document that penalty in employment contracts increases employee performance due to loss aversion. The study, however, demonstrates that the positive effect of penalty is not sustained in a gift-exchange contract. Specifically, the study's experimental results provide evidence that the availability of penalties can psychologically change the way employers perceive their decisions on offering generous wages (i.e. trust) and consequently reduce employees' reciprocation of high effort levels. Yet, the authors propose a two-way communication as a restorative mechanism for the lost trust. Implications for theory and practice are discussed.

The aim of this study was to analyze the effects of carbon reduction efforts and preservation efforts on system benefits in the cold chain industry of fresh products.

This study develops an optimal decision game model for the fresh products in the cold chain, incorporating the retailer's preservation effort and the supplier's carbon emission reduction effort. It quantifies the relationship between carbon emission reduction effort, preservation effort and system profit. The model considers parameters like carbon trading price, consumer low-carbon preference and consumer freshness preference, reflecting real-world conditions and market trends. Numerical simulations are conducted by varying these parameters to observe their impact on system profit.

Under the carbon cap-and-trade policy, the profit of the fresh cold chain system is higher than that of the fresh cold chain system without carbon constraints, and the profit of the supplier under decentralized decision-making is increased by nine times in the simulation results. The increase in carbon trading prices can effectively improve the freshness level of fresh products cold chain, carbon emission reduction level and system profit.

This study comprehensively considers the factors of freshness and carbon emission reduction, provides the optimal low-carbon production decision-making reference for the fresh food cold chain and promotes the sustainable development of the fresh food cold chain.

Research in the business-to-business (B2B) and user entrepreneurship literature agrees that “user-driven” perspectives allow entrepreneurs to develop innovative products superior to conventional products. Other researchers argue that such “user-driven” products have limited success and limited impact in certain markets (e.g. niche and industrial markets). This study aims to understand the extent to which user input or co-creation becomes critical in determining product performance.

The key informant approach is used for data collection. Data were collected using a survey instrument via an online panel. Existing scales are used to measure all the focal constructs. Partial least square-based structural equation modeling was used to check for the psychometric properties of the scales and test the hypotheses.

The results indicate that user entrepreneurship is significantly related to firm collaboration efforts and customer collaboration efforts in the B2B market. Both firm collaboration efforts and customer collaboration efforts are significantly related to product performance and mediate the relationship between user entrepreneurship and product performance. Also, findings show that there is an “n” relationship between firm collaboration efforts and product performance.

This study supports the concerns raised by researchers about the dark side of value co-creation and highlights that value co-creation can impede product performance when user entrepreneurs lay too much emphasis on the collaboration processes.

Artificial intelligence (AI) is shaping the future of the marketing world. This study is the first to examine the effect of AI marketing efforts, brand experience (BE) and brand preference (BP) in light of the stimulus-organism-response (SOR) model.

The data collected from 398 participants by the questionnaire method were analyzed by SEM (structural equation modeling) using Smart PLS 4.0 and IBM SPSS 26 programs.

We find that four SOR elements of AI marketing efforts (information, interactivity, accessibility and personalization) positively impact bank customer BE, BP and repurchase intention (RPI). Further, we find that BE plays a mediator role in the relationship between AI marketing efforts, RPI and BP.

The findings of the study have significant implications for the bank marketing literature and the banking industry, given the limited evidence to date regarding AI marketing efforts and bank–customer relationships. Moreover, the study makes important contributions to the AI marketing and brand literature and helps banks increase customer experience with artificial intelligence activities and create long-term relationships with customers.

To remain sustainable, open source software (OSS) projects must attract new members—or newcomers—who make contributions. In this paper, the authors develop a set of hypotheses based on the knowledge barriers framework that examines how OSS communities can encourage contributions from newcomers.

Employing longitudinal data from the source code repositories of 232 OSS projects over a two-year period, the authors employ a Poisson-based mixed model to test how community characteristics, such as the main drivers of knowledge-based costs, relate to newcomers' contributions.

The results indicate that community characteristics, such as programming language choice, documentation effort and code structure instability, are the main drivers of knowledge-based contribution costs. The findings also suggest that managing these costs can result in more inclusive OSS communities, as evidenced by the number of contributing newcomers; the authors highlight the importance of maintaining documentation efforts for OSS communities.

This paper assumes that motivational factors are a necessary but insufficient condition for newcomer participation in OSS projects and that the cost to participation should be considered. Using the knowledge barriers framework, this paper identifies the main knowledge-based costs that hinder newcomer participation. To the best of the authors' knowledge, this is the first empirical study that does not limit data collection to a single hosting platform (e.g., SourceForge), which improves the generalizability of the findings.

This paper aims to explore the promotion decisions of heterogeneous sellers on a decentralized platform under competitive conditions and analyze how seller behaviors impact platform profit, seller revenue, buyer surplus and social welfare.

This paper considers a Cournot model consisting of a platform charging a commission rate and two sellers with different conversion rates and browsing costs. Promotion efforts by sellers can increase traffic, but they also incur promotion costs for sellers. The sellers decide on promotion effort by weighing these two effects. The authors also explore the equilibrium when the platform charges a fixed usage fee.

The seller’s profit improves as its conversion rate increases and worsens as browsing costs increase. Also, increasing the commission rate charged by the platform makes the seller invest less in promotional efforts. Therefore, the platform must consider this trade-off to determine an optimal rate. The analysis shows that the seller with a high conversion rate and high browsing cost plays a greater role in generating more overall revenue. When the market favors such a seller, the platform tends to charge less in order not to impair its profitability.

This paper incorporates conversion rate, buyer’s browsing cost, unit promotion cost and the fee charged by the platform into the model to study sellers’ promotion decisions on decentralized platforms.

The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.

This study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.

The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.

This study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.

This study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.

In the present study, we examined how effectively online travel agencies (OTAs) handle negative e-word-of-mouth on social media platforms like Facebook, Twitter, and Instagram. We collected data from 497 participants using survey method. To test the hypotheses formulated from the existing literature, structural equation modeling was adopted in this study. The results from structural equation modeling indicate effective handling of the negative e-word of mouth (e-WOM) on social media websites significantly affects customer satisfaction and repurchase intention. The current research work provides insight into social media recovery efforts and service fairness when handling negative e-WOM. The study recommends that customers can distinguish the differences between general efforts and adaptive complaint-handling efforts, and dissimilarities may influence satisfaction, repurchase intentions, etc. Although empathy, apology, responsiveness, and paraphrasing are considered pioneer strategies in complaint handling, customers' negative e-WOM, and firms' recovery management, but the current study is among a few to categorize OTAs' handling of negative e-WOM and complaint handling efforts in the social media environment.

The Internet of Things (IoT) offers substantial potential for improving efficiency and effectiveness in various applications, notably within the domain of smart construction. Despite its growing adoption within the Architecture, Engineering, and Construction (AEC) industry, its utilization remains limited. Despite efforts made by policymakers, the shift from traditional construction practices to smart construction poses significant challenges. Consequently, this study aims to explore, compare, and prioritize the determinants that impact the acceptance of the IoT among construction practitioners.

Based on the integrated model of Unified Theory of Acceptance and Use of Technology (UTAUT2), Task-Technology Fit (TTF), and perceived risk. A cross-sectional survey was administered to 309 construction practitioners in China, and the collected data were analyzed using structural equation modeling (SEM) to test the proposed hypotheses.

The findings indicate that TTF, performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, and perceived risk exert significant influence on construction practitioners’ intention to adopt IoT. Conversely, social influence and habit exhibit no significant impact. Notably, the results unveil the moderating influence of gender on key relationships – specifically, performance expectancy, hedonic motivation, and habit – in relation to the behavioral intention to adopt IoT among construction practitioners. In general, the model explains 71% of the variance in the behavioral intention to adopt IoT, indicating that the independent constructs influenced 71% of practitioners’ intentions to use IoT.

These findings provide both theoretical support and empirical evidence, offering valuable insights for stakeholders aiming to gain a deeper understanding of the critical factors influencing practitioners’ intention to adopt IoT. This knowledge equips them to formulate programs and strategies for promoting effective IoT implementation within the AEC field.

This study contributes to the existing literature by affirming antecedents and uncovering moderators in IoT adoption. It enhances the existing theoretical frameworks by integrating UTAUT2, TTF, and perceived risk, thereby making a substantial contribution to the advancement of technology adoption research in the AEC sector.