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This paper aims to carry out a comprehensive overview of the academic debate on utilities’ non-financial reporting by highlighting the main issues and the emerging gaps.

Using a structured literature review, this study identifies the state of the art, maps the evolution of non-financial reporting in utility companies and reveals unexplored issues and aspects.

A critical analysis of the existing academic debate shows the development of utilities’ non-financial reporting literature and the focus of this debate. It provides insight into how utilities pay attention to non-financial reporting, what role this plays in corporate actions and relationships with stakeholders and what research gaps need further investigation.

This study provides some useful recommendations to practitioners and regulators to be more conscious of the weaknesses and criticalities of utilities’ non-financial reporting and to address them when building such reporting. However, this study considered only articles published in peer-reviewed academic journals.

A comprehensive literature review in the utilities’ non-financial reporting area is timely, given the increase in this type of reporting. The study has an original focus and develops an analytical framework highlighting the peculiarities of utilities.

Literature on psychology highlights four traits that shape an amoral and antisocial personality: Machiavellianism, narcissism, psychopathy and sadism. Together, these personality traits form the Dark Tetrad. In this study, the standard intertemporal utility maximization model is reassessed from the point of view of a representative economic agent endowed with the Dark Tetrad personality traits.

The approach followed in this paper consists of identifying how each of the Dark Tetrad traits might be logically associated with the dynamic utility problem, as well as exploring, in the context of the model, the implications, for consumption and utility, of admitting the presence of such traits in individuals’ personalities.

It is found that, typically, dark personalities penalize consumption growth, even when such traits are interpreted directly and positively contributing to the utility of the agent. It is also found that in economies with two or more interacting agents, the dark traits might have a mutually destructive nature.

Economics is going through a smooth revolution in the direction of becoming an eminently behavioral science. Most of the traditional economic models, based on the idea of the hyper-rational agent, are being replaced or complemented by a different view of the homo-economicus, in which, among other things, personality matters. This paper offers a novel contribution in this direction.

The Multi-Stakeholder Recommendation System learns consumer and producer preferences to make fair and balanced recommendations. Exclusive consumer-focused studies have improved the recommendation accuracy but lack in addressing producers' priorities for promoting their diverse items to target consumers, resulting in minimal utility gain for producers. These techniques also neglect latent and implicit stakeholders' preferences across item categories. Hence, this study proposes a personalized diversity-based optimized multi-stakeholder recommendation system by developing the deep learning-based diversity personalization model and establishing the trade-off relationship among stakeholders.

The proposed methodology develops the deep autoencoder-based diversity personalization model to investigate the producers' latent interest in diversity. Next, this work builds the personalized diversity-based objective function by evaluating the diversity distribution of producers' preferences in different item categories. Next, this work builds the multi-stakeholder, multi-objective evolutionary algorithm to establish the accuracy-diversity trade-off among stakeholders.

The experimental and evaluation results over the Movie Lens 100K and 1M datasets demonstrate that the proposed models achieve the minimum average improvement of 40.81 and 32.67% over producers' utility and maximum improvement of 7.74 and 9.75% over the consumers' utility and successfully deliver the trade-off recommendations.

The proposed algorithm for measuring and personalizing producers' diversity-based preferences improves producers' exposure and reach to various users. Additionally, the trade-off recommendation solution generated by the proposed model ensures a balanced enhancement in both consumer and producer utilities.

We attempt to analyze the impact of retailer’s rebate strategy on consumer reviews and retailer’s profits.

Retailers' rebates have a chance to affect sales and their profits by encouraging customers to submit product reviews. To investigate the impact of retailer’s rebate strategy on consumer reviews and retailer’s profits, we describe the consumer’s utility function and the number of consumer-written reviews by introducing the concepts of product demand mismatch and consumer review effort, then develop a two-stage model of the retailer’s rebate strategy and examine how the retailer’s rebate affects online reviews, the consumer’s perceived utility and the retailer’s profit. Finally, a number case verifies the validity and rationality of the proposed model.

The results show that the rebate strategy can effectively reduce consumer dissatisfaction caused by excessive product demand mismatch, improve the consumer utility, prompt more positive comments, and thus increase product sales.

In this paper, we focus on the impact of retailers' rebate strategy on consumer purchase decisions. The research can accurately reflect the influence of online reviews on consumers and retailers, assisting merchants in making the best selections. The analysis indicates that the retailer’s rebate strategy can have a direct impact on consumers' evaluation choices and product sales.

This study aims to establish an analytical framework to help investors accommodate their environmental, social, and corporate governance (ESG) preferences. The analytical solutions were complemented by empirical analyses to shed light on their benefits and tractability.

This study proposes an expected multi-attribute utility analysis for ESG investors in which stocks can be treated as more green or less green (brown) than the market, represented by an index, all modeled in a one-factor structure. The solution is found via the Hamilton-Jacobi-Bellman (HJB) equation with proper treatment of various sources of risk. For the empirical analysis, we use the RepRisk Rating of US stocks from 2010 to 2020 to select companies that are representative of various ESG ratings.

This study finds closed-form solutions for optimal allocations, wealth and value functions. Our empirical analysis reveals drastic increases in wealth allocation toward high-rated ESG stocks for ESG-sensitive investors, even as the overall level of pecuniary satisfaction remains unchanged.

This study broadens the existing analytical framework by introducing a market portfolio along with green and brown stocks. As by-products, we first demonstrate that investors do not need to reduce their pecuniary satisfaction to increase green investment. Second, we propose a parameterization to capture investors' preferences for green assets over brown or market assets, independent of asset performance.

Art-infusion has become a common practice among brands across the globe. This is because marketers leverage the uniqueness and prestige of arts to earn superior profits. Hence, this research aims to understand and measure consumers' willingness to pay (WTP) for art-infused products.

A questionnaire was designed based on conjoint analysis and was responded to by 470 respondents from India. The estimation of preference functions in conjoint analysis was intended to use orthogonal arrays to measure WTP.

The study reveals consumers' utility and WTP for different art-infused products. The results indicated that consumers have the highest WTP for products that have artwork dominated by the visual elements of colour, shapes and space.

The paper presents valuable findings for marketers to develop their product design and earn superior profits.

This is the first study in the domain of the art infusion phenomenon that measures WTP for non-luxury art-infused products. Also, this is the first study to measure WTP for different kinds of art forms.

Customers will develop a stronger desire to purchase when more people are waiting in line for service due to the herding effect. However, this also leads to longer queue times, causing customers to experience a waiting patience time. This study examines these two psychological aspects of delay-sensitive customers in service systems, considering both homogeneous and heterogeneous customer scenarios to explore the optimal pricing strategy for service providers.

Using queueing theory, we construct and optimally solve the customer's service utility function and the service provider's service revenue function. Further, the model is extended to account for heterogeneous customers, solving the utility and revenue functions accordingly.

Results show that service revenue increases with the intensity of herding behavior and the length of patience time. If customers have low herding intensity and short patience time, the service provider only needs to serve a portion of the customers. For heterogeneous customers, if a large proportion exhibits high herding intensity, the service provider should focus on serving them. Otherwise, the service provider should serve all high-intensity herding customers while striving to attract low-intensity herding customers.

This paper considers the combined utility of multiple customer psychology and examines homogeneous and heterogeneous customers. The findings provide valuable managerial insights for service providers' pricing and service strategies.

This paper aims to examine whether the instructional message used to introduce game-based learning (GBL), prior knowledge, flow and prior gaming experience have an impact on secondary students’ learning outcomes, perceived game utility and motivation to use the game.

Fifty-four 9th grades students enrolled in this quasi-experimental study. Both groups played the same game. The experimental group was told that they would be playing a game, whereas the control group was told that they would be doing an exercise. The game was designed to promote knowledge recall about familiar mathematical concepts. Prior knowledge was assessed. An immediate and a two-days delayed post-evaluated learning outcomes. Flow, prior gaming experience, perceived game utility and motivation to use the game were assessed with questionnaires after playing.

Introducing the GBL activity as a game increased immediate learning outcomes for low knowledgeable students, but these benefits did not extend to longer term. Indeed, when the GBL activity was presented as an exercise, low higher knowledgeable students obtained poorer longer-term learning outcomes than more knowledgeable ones, whereas no difference appeared when the GBL was introduced as an exercise. Prior gaming experience and the type of instructional message used to introduce a GBL activity positively influenced perceived game utility and motivation to use the game.

This study is the first to highlight that the way a GBL activity is framed affects differently low and higher knowledgeable students’ learning outcomes and influences perceived game utility and motivation to use the game.

We aim to generalize the continuous-time principal–agent problem to incorporate time-inconsistent utility functions, such as those of mean-variance type, which are prevalent in risk management and finance.

We use recent advancements of the Pontryagin maximum principle for forward-backward stochastic differential equations (FBSDEs) to develop a method for characterizing optimal contracts in such models. This approach addresses the challenges posed by the non-applicability of the classical Hamilton–Jacobi–Bellman equation due to time inconsistency.

We provide a framework for deriving optimal contracts in the principal–agent problem under hidden action, specifically tailored for time-inconsistent utilities. This is illustrated through a fully solved example in the linear-quadratic setting, demonstrating the practical applicability of the method.

The work contributes to the existing literature by presenting a novel mathematical approach to a class of continuous time principal–agent problems, particularly under hidden action with time-inconsistent utilities, a scenario not previously addressed. The results offer potential insights for both theoretical development and practical applications in finance and economics.

This study aims to reveal consumer purchase intentions for Software-as-a-Service (SaaS) lifetime deals and the role of service offerings in shaping these intentions.

Lifetime deals − an aggressive market penetration strategy − have the potential to allow startups to gain market share, user base and the cash necessary for growth. However, startups need to mitigate consumer concerns for which service offering design plays a key role. Drawing on expectancy-value and signaling theories, this study developed a research model and then conducted empirical research on 2,173 consumers via choice-based conjoint analysis to reveal the critical service offering attributes for consumer utility in lifetime deals in the SaaS presentation tool market context. After using the hierarchical Bayes model to derive each respondent’s part-worth utilities for service offering attributes, the hypotheses were tested via the factor score regression method.

The results show that the service offering attributes of low price, refund option, human support and feature updates enhance consumer utility in SaaS lifetime deals. Three of these four attributes, namely, low price, refund option and feature updates, enhance consumers' purchase intentions by reducing their concerns about the service’s performance, seller and lifespan, respectively.

This study elucidates consumer purchase intentions for SaaS services in digital marketplaces. By investigating a widespread market entry strategy − lifetime deals − it shows consumer preferences and behavior for these deals in the fast-growing online tools market. This study also shows how startups can use lifetime deals through a well-designed service offering to mitigate various consumer concerns.