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This research draws on drive reduction theory and mental accounting theory to understand how the prospect of reselling used items can influence consumer feelings of consumption guilt and impact their willingness to purchase new products.

We conducted two studies with between-subjects designs to explore this relationship. In Study 1, we examined the correlation between consumers' perceived guilt and their willingness to buy a new product, considering their awareness of the product’s resale potential. Study 2 delved into the aspect of reselling a similar old product already owned by the consumer.

The findings suggest three key insights. First, consumers' awareness of resale potential significantly affects their guilt perception and purchasing decisions. Second, the resale reference price (RRP) can decrease guilt perception but increase the intention to buy a new product. Lastly, when consumers are aware of the resale value of a previously owned product that is similar to the desired new product, the effect of the RRP on their purchasing intent is mediated by consumer guilt.

This research fills a theoretical gap by empirically exploring the emotional motivations behind consumer resale behavior. It presents a novel perspective on how resale activities can shape feelings of guilt and impact purchasing decisions. This offers important implications for understanding the dynamics of consumer behavior in the second-hand market.

The aim of this study was to determine if there are asymmetries in the pass-through of West Texas Intermediate (WTI) crude oil prices to its derivatives (diesel and gasoline) in the Brazilian market.

Initially, the future WTI oil price series was analyzed using the self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) non-linear models. Subsequently, the threshold autoregressive error-correction model (TAR-ECM) and Markov-switching model were used.

The findings indicated high prices throughout 2008 due to the subprime crisis. The findings indicated high prices throughout 2008 due to the subprime crisis. The results indicated that there is long-term pass-through of oil prices in both methods, suggesting an equilibrium adjustment in the prices of diesel and gasoline in the analyzed period. Regarding the short term, the variations in contemporary crude oil prices have positive effects on the variations in fuel prices. Lastly, this behavior can partly be explained by the internal price management structure adopted during almost all of the analyzed period.

This paper contributes to the literature at some points. The first contribution is the modeling of the oil price series through non-linear models, further enriching the literature on the recent behavior of this time series. The second is the simultaneous use of the TAR-ECM and Markov-switching model to capture possible short- and long-term asymmetries in the pass-through of prices, as few studies have applied these methods to the future price of oil. The third and main contribution is the investigation of whether there are asymmetries in the transfer of oil prices to the price of derivatives in Brazil. So far, no work has investigated this issue, which is very relevant to the country.

The purpose of this study was to investigate the elements of the triadic framework (e.g. consumer motives, activities, resources and capabilities) and their influence on consumer attitudes and the theory of reasoned action (TRA) elements of subjective norms and eventual purchase intention of secondary sneakers.

The framework draws from two models, the triadic framework for collaborative consumption and the TRA. Data was collected using an online survey, which consisted of demographic questions and reliable scales. The hypothesized relationships were tested through structural equation modeling.

The findings indicate that reducing risks and the customer activity of providing information was significant in influencing consumers' attitudes. However, the customer motives of economic and environmental as well as technical skills were not significant in influencing attitudes.

This study attempts to inform secondary and full-priced retailers offering used products strategies to provide the best experience to millennial and generation Z consumers to ensure they can attract and retain them.

It can be stated with a high degree of certainty that sneaker resale remains one of the most permanent trends in the market today with sneaker resale is significantly outperforming the broader ecommerce ecosystem suggesting more research into the motivations of secondary sneaker consumption.

This paper aims to study the joint decision making of advance selling and service cancelation for service provides with limited capacity when consumers are overconfident.

For the case in which consumers encounter uncertainties about product valuation and consumption states in the advance period and are overconfident about the probability of a good state, we study how the service provider chooses the optimal sales strategy among the non-advance selling strategy, the advance selling and disallowing cancelation strategy, and the advance selling and allowing cancelation strategy. We also discuss how overconfidence influences the service provider’s decision making.

The results show that when service capacity is sufficient, the service provider should adopt advance selling and disallow cancelation; when service capacity is insufficient, the service provider should still implement advance selling but allow cancelation; and when service capacity is extremely insufficient, the service provider should offer spot sales. Moreover, overconfidence weakens the necessity to allow cancelation under sufficient service capacity and enhances it under insufficient service capacity but is always advantageous to advance selling.

The obtained results provide managerial insights for service providers to make advance selling decisions.

This paper is among the first to explore the effect of consumers’ overconfidence on the joint decision of advance selling and service cancelation under capacity constraints.

Different information sharing mechanisms and online platform information sharing to different charging models are compared and analyzed.

This paper uses the Stackelberg game model to study the demand information sharing and pricing decisions.

The results show that: (1) the retailer's pricing strategy is the highest when both of them obtain information, while the manufacturer's pricing strategy is affected by the related attributes of different products, such as the sensitivity of consumers to product prices; (2) in the online platform sales model, the demand information data sharing owned by the online platform can bring more expected profits to the whole supply chain and the members of the supply chain, and the higher the accuracy of the information, the higher the expected profit; (3) when the cost of obtaining demand information is zero, that is, the online platform shares the information data about market demand free of charge, the retailer and manufacturer tend to obtain information; (4) for the online platform, charging a certain fee can achieve higher expected profits than free sharing.

Based on the single platform online sales model, this paper uses the Stackelberg game model to study the demand information sharing and pricing decision of a manufacturer and a retailer selling products through the same online platform.

This paper aims to demonstrate that Facebook likes affect outcomes in nonprofit settings. Specifically, Facebook likes influence affinity to nonprofits, which, in turn, affects fundraising outcomes.

The authors report three studies that establish that relationship. To examine social contagion, Study 1 – an auction field study – relies on selling artwork created by underprivileged youth. To isolate signaling, Study 2 manipulates the number of total Facebook likes on a page. To isolate commitment escalation, Study 3 manipulates whether a participant clicks a Facebook like.

The results show that Facebook likes increase willingness to contribute in nonprofit settings and that the process goes through affinity, as well as through Facebook impressions and bidding intensity. The total number of Facebook likes has a direct signaling effect and an indirect social contagion effect.

The effectiveness of the proposed mechanisms is limited to nonprofit settings and only applies to short-term effects.

Facebook likes serve as both a quality signal and a commitment mechanism. The magnitude of commitment escalation is larger, and the relationship is moderated by familiarity with the organization. Managers should target Facebook likes at those less familiar with the organization and should prioritize getting a potential donor to leave a like as a step leading to donation, in essence mapping a donor journey from prospective to active, where Facebook likes play an essential role in the journey. In a charity auction setting, the donor journey involves an additional step of bidder intensity.

The approach the authors study is shown effective in nonprofit settings but does not appear to extend to corporate social responsibility more broadly.

To the best of the authors’ knowledge, this study is the first investigation to map Facebook likes to a seller’s journey through signals and commitment, as well as the only investigation to map Facebook likes to charity auctions and show the effectiveness of this in the field.

This paper aims to establish a fundamental and comprehensive understanding of non-fungible tokens (NFTs) by identifying and structuring common characteristics within a taxonomy. NFTs are hyped and increasingly marketed as essential building blocks of the Metaverse. However, the dynamic evolution of the NFT space has posed challenges for those seeking to develop a deep and comprehensive understanding of NFTs, their features and their capabilities.

Utilizing common guidelines for the creation of taxonomies, the authors developed (over 3 iterations), a multi-layer taxonomy based on workshops and interviews with 11 academic and 15 industry experts. Through an evaluation of 25 NFTs, the authors demonstrate the usefulness of the taxonomy.

The taxonomy has 4 layers, 14 dimensions and 42 characteristics, which describe NFTs in terms of reference object, token properties, token distribution and realizable value.

The authors' framework is the first to systematically cover the emerging NFT phenomenon. This framework is concise yet extendible and presents many avenues for future research in a plethora of disciplines. The characteristics identified in the authors' taxonomy are useful for NFT- and Metaverse-related research in finance, marketing, law and information systems. Additionally, the taxonomy can serve as an information source for policymakers as they consider NFT regulation.

This study aims to predict stock market crashes identified by the CMAX approach (current index level relative to historical maximum) during periods of global and local events, namely the subprime crisis of 2008, the political and social instability of 2011 and the COVID-19 pandemic.

Over the period 2004–2020, a log-periodic power law model (LPPL) has been employed which describes the price dynamics preceding the beginning dates of the crisis. In order to adjust the LPPL model, the Global Search algorithm was developed using the “fmincon” function.

By minimizing the sum of square errors between the observed logarithmic indices and the LPPL predicted values, the authors find that the estimated parameters satisfy all the constraints imposed in the literature. Moreover, the adjustment line of the LPPL models to the logarithms of the indices closely corresponds to the observed trend of the logarithms of the indices, which was overall bullish before the crashes. The most predicted dates correspond to the start dates of the stock market crashes identified by the CMAX approach. Therefore, the forecasted stock market crashes are the results of the bursting of speculative bubbles and, consequently, of the price deviation from their fundamental values.

The adoption of the LPPL model might be very beneficial for financial market participants in reducing their financial crash risk exposure and managing their equity portfolio risk.

This study differs from previous research in several ways. First of all, to the best of the authors' knowledge, the authors' paper is among the first to show stock market crises detection and prediction, specifically in African countries, since they generate recessionary economic and social dynamics on a large extent and on multiple regional and global scales. Second, in this manuscript, the authors employ the LPPL model, which can expect the most probable day of the beginning of the crash by analyzing excessive stock price volatility.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

To study these issues, the authors chose a GFSC with one producer and one material supplier as research object, the supplier will offer green material to the producer and the producer will make green food using green production technology. Then, the authors proposed that consumers' perceived value was determined by the trustworthiness levels of the related green and quality-safety information provided by the supplier and the producer. Then, considering the trustworthiness levels of the green and quality information provided by the supplier and the producer, the authors improved the demand function. Afterwards, we constructed four investment models and their income models are built and then a cost-sharing and revenue-sharing contract (hereafter, CSRS) was adopted to coordinate the GFSC.

With the growth of consumers environmental awareness and life level, consumers' requirements for green and high quality food are growing. In recently years, to increase consumers' perceived trustworthiness on the product greenness and quality levels, stakeholders in green food supply chain (hereafter, GFSC) start to adopt the blockchain-based traceability system (hereafter, BLTS). For investors, they need to know the investment conditions and how to coordinate the GFSC.

(1) When the revenue-sharing coefficient is less than three-fourths and higher then a certain vaule, the cost-sharing and revenue-sharing contract can make the GFSC coordinate. (2) The investment cost threshold of the BLTS has a positive relationship with the trustworthiness improvement levels of the green and quality information, the green degree of food products and the quality of food products. (3) In the proposed four investment situations, as the growth of consumers perceived credibility coefficient about the greenness information and the quality information, chain members' revenues will increase. In addition, comparing with co-investing the BLTS, benefits of chain members are lower than them in the sole investment model.

(1) The demand function we proposed can help chain members forecast market demand to support production or ordering decisions. (2) The investment decision policies can offer a theoretical reference for chain members to use the BLTS. (3) The CSRS will offer the theoretical reference for coordinating the supply chain after using the BLTS. Furthermore, our study method can be referenced by other scholars. (4) The study method can offer a method reference for researchers who do a similar discussion in a manufacturing supply chain. Although, our research cannot guide the industrial practices, it can serve as a reference of the similar research in industry.