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The purpose of this study is to contribute to previous research on customer relationships by quantitatively examining differences in the monetary benefits obtained by consumers using social and commercial relationships to make purchases from small and medium-sized enterprises (SMEs).

Customer transaction and relationship data from an SME in the USA is used to quantitatively assess the value of different marketplace relationships in an entrepreneurial context. Tobit regression is used to empirically model and test the impact of specific relationship characteristics on customer discounts.

Customers using social connections to make purchases obtain significantly larger discounts than customers using commercial connections; customers using direct connections attain significantly larger discounts than consumers using indirect connections (referrals). Interestingly, when examined by connection type, direct and indirect connections do not produce significant differences for social connections, yet they yield notable differences for commercial connections. The findings provide valuable insights to entrepreneurs for understanding and managing customer relationships.

This study empirically demonstrates that social relationships can be both prevalent and influential in the marketplace. The methodology used to quantitatively assess the monetary value associated with different methods of engaging with SMEs allows objective comparisons among different types of customer relationships. Quantification also allows important relationship characteristics to be empirically examined, including how the relationships compare to one another and to nonpersonal marketing activities. Ultimately, these novel contributions generate important insights to help marketers and entrepreneurs better understand customer relationships.

Food waste is a severe problem affecting the supply chain due to its significant adverse social and environmental effects. Even if the topic is hotly debated in the literature, there is a lack of research about the success factors influencing food waste prevention initiatives retailers undertake.

The research analyzes how several variables (i.e. product-related variables and technology-enabling variables) might impact the success of the sales of products close to the expiration date that is sold at a discounted price. Data from 390.000 products sold at a discounted price in 2020 and 2021 by a large Italian food retailer were examined with a regression analysis.

The results highlight that both product-related and technology-enabling variables influence the success of food prevention initiatives aimed at selling products close to the expiration date at a discounted price. In particular, the authors stress the importance of digital technologies in supporting food waste prevention initiatives.

The study offers several practical implications for managers in structuring a waste prevention initiative. The introduction of digital technologies, the monitoring of specific variables or the ability to find synergies with other food waste prevention initiatives are discussed to support retailers in reducing food losses.

The paper is focused on the retailer perspective, which is barely investigated due to the difficulty in finding data.

This study highlights the impact of mental accounts on a user's decision-making regarding payment schemes and aims to determine the pricing strategy for the first-enjoy-after-pay service offered by the two-sided media platforms.

This study establishes a game-theoretic model and utilizes backward induction to derive the equilibrium price by maximizing the monopolist's profit.

The findings indicate that the conditions for a two-sided media platform to offer the first-enjoy-after-pay service depend on the trade-off between pleasure attenuation and pain buffering and the effect of time discounts. Moreover, the authors found that the time discount is a critical factor in determining pricing strategies under various payment schemes offered by the platform.

This work adopts a uniform pricing strategy for users who opt for either immediate or post-payment schemes. Nevertheless, it is important to note that this approach has limitations in terms of offering discriminatory pricing for those who choose both payment schemes.

This analytical work provides valuable insights for two-sided media platforms to optimize their payment scheme strategies and pricing considering the influence of a user's mental account.

In a two-sided media platform, the authors provide applicable conditions for the platform to offer first-enjoy-after-pay service considering the effect of mental accounts. Further, the authors show the optimal pricing strategy under different payment schemes provided by the platform.

This research investigates the factors influencing consumers' intention to purchase e-deals from group buying websites, focussing on e-deal proneness, price consciousness and anticipatory regret.

Three studies (n = 539) were conducted using data collected from an online consumer panel and tested via structural equation modelling and PROCESS macro in SPSS.

The findings suggest that subjective norms, perceived behavioural control and attitudes positively influence consumers' e-deal purchase intention. Additionally, price consciousness amplifies the relationship between consumers' e-deal proneness and purchase intention, and price-conscious respondents are more likely to have the intention to buy e-deals when faced with some form of anticipatory regret.

Based on the research findings, practitioners are advised to prioritise social norms and entertainment value when promoting the attractiveness of e-deals, using strategies such as social media and influencer marketing. Brands should also emphasise the value of e-deals by showcasing comparative price savings and discounts to motivate consumers to buy.

This paper addresses an interesting and practical issue related to the effects of group buying websites, focussing on e-deal proneness, price consciousness and anticipatory regret.

This study explores consumers' perceptions of purchasing fast food items through online platforms. The central idea of this research is to practically assess the various elements impacting the consumers’ perceptions of online purchasing of fast food items and find out the factors with high importance and performance value.

A quantitative research approach was used to collect data from 402 participants in the form of a pen-and-paper-based method using a 5-point Likert scale. The collected data were analyzed using structural equation modeling (SEM) and importance-performance analysis. Theory of planned behavior and technology acceptance model form the basis for this research.

The findings indicate that constructs such as convenience, perceived quality and perceived healthiness positively influence consumers' perceptions of online purchasing of fast food items. On the other hand, competitive prices, discounts and promotions (CPDP) and online shopping experience have no significant impact on perceived value for money.

A constraint of this study is that it was done in a particular geographical location, which restricts the generalizations of the findings. The study only examined consumers' perceptions of online fast food purchasing, and future research could explore consumers' actual behaviors toward personalized fast food recommendations by online sellers.

The research supports and extends the existing literature by comprehensively understanding consumers' perceptions of purchasing fast food online. These findings can help online fast food sellers improve their services and develop targeted marketing strategies.

Inaction inertia occurs when people are less likely to act on a similar but inferior option after missing a superior opportunity, compared to if they had not missed out. This study aims to explore how promotional formats and their sequence affect the inaction inertia effect in online shopping, under the assumption of economic equivalence.

The authors performed two online experiments and analyzed the data by analysis of variance.

The findings indicate that, under the premise of economic equivalence: Monetary promotions exhibit a higher inaction inertia effect on consumers than nonmonetary promotions. When consumers miss a more favorable promotion and subsequently encounter a relatively less attractive one presented in a different promotional format, the inaction inertia effect is lower than when reencountering the same promotion format. When consumers miss a better monetary promotion and presently encounter a relatively less attractive nonmonetary promotion, the inaction inertia effect is lower than when they miss a superior nonmonetary promotion and currently encounter a relatively less attractive monetary promotion.

This study reveals the sequence effects of promotional formats, indicating that nonmonetary promotions following monetary ones effectively reduce inaction inertia. A strategically sequenced set of formats enhances consumer recommendations, mitigating inaction inertia. These findings open new research paths, providing insights into the impact of promotional format sequences on the inaction inertia effect. Consequently, this knowledge helps e-retailers in implementing effective promotional strategies and driving online purchases.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

This study aims to examine the reaction of anchor investors (AIs) to pre-IPO earnings management (EM). The authors use the unique detailed bid data from the Indian anchor experiment. The authors also study the reputed AIs’ EM detection ability and pricing behavior in response to pre-IPO EM.

The authors use unique AI bid data for 169 Indian IPO firms. Utilizing the logistic regression and Tobit regression models with industry and year-fixed effects, the authors examine the relationship between various measures of AI participation and proxies of short-term and long-term discretionary accruals.

The authors document that pre-IPO EM is positively associated with the likelihood of anchor backing but negatively related to the likelihood of reputed anchor backing. The findings indicate that AIs are misled by pre-IPO EM, but reputed AIs are not. The authors also observe that reputed AIs, compared to the non-reputed, pay less than the upper band with increasing EM. The findings are robust to using various AI measures and EM proxies.

The findings have significant implications for regulators in the implementation of AI concept in non-anchor markets and better implementation of policies in existing anchor settings. Findings can also be relevant for non-institutional investors in the IPO domain.

This is one of the few studies on institutional investors' IPO bidding behavior in response to pre-IPO EM. However, this is the first study to analyze AIs' IPO bidding behavior in response to pre-IPO EM.

Surprisingly little is known of the various methods of security analysis used by financial analysts with industry-specific knowledge. Financial analysts’ industry knowledge is a favored and appreciated attribute by fund managers and institutional investors. Understanding analysts’ use of industry-specific valuation models, which are the main value drivers within different industries, will enhance our understanding of important aspects of value creation in these industries. This paper contributes to the broader understanding of how financial analysts in various industries approach valuation, offering insights that can be beneficial to a wide range of stakeholders in the financial market.

This paper systematically reviews existing research to consolidate the current understanding of analysts’ use of valuation models and factors. It aims to demystify what can often be seen as a “black box”, shedding light on the valuation tools employed by financial analysts across diverse industries.

The use of industry-specific valuation models and factors by analysts is a subject of considerable interest to both academics and investors. The predominant model in several industries is P/E, with some exceptions. Notably, EV/EBITDA is favored in the telecom, energy and materials sectors, while the capital goods industry primarily relies on P/CF. In the REITs sector, P/AFFO is the most commonly employed model. In specific sectors like pharmaceuticals, energy and telecom, DCF is utilized. However, theoretical models like RIM and AEG find limited use among analysts.

This is the first paper systematically reviewing the research on analyst’s use of industry-specific stock valuation methods. It serves as a foundation for future research in this field and is likely to be of interest to academics, analysts, fund managers and investors.