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The purpose of this paper is to investigate how fairness evaluations are constructed in a B2B context.

This paper conducts a field study of Air Greenland and its internal and external customers based on strong structuration theory (Stones, 2005). The authors employ context and conduct analysis to analyze how fairness evaluations emerge across four levels of structuration.

The paper finds that fairness evaluations emerge as a result of the interaction between external institutional pressures, agents' internal structures, and situated reflection and outcomes. The construction of fairness evaluations was embedded in contradictory institutional structures, where groups of actors constructed different evaluations of fair profits, procedures and prices. Actors furthermore worked on changing position-practice relations which shifted relations, external structures and affected outcomes and fairness evaluations.

This paper offers a conceptualization of embedded agency as emerging across the four levels of structuration. This contributes to debates in strong structuration theory through conceptualizing and analyzing how actors may be both be constrained and oriented by structures while reflexively adapting structures across the four levels of structuration. The paper extends extant pricing fairness research by illustrating how actors' construction of fairness flexibly develop fairness evaluations while responding to legitimacy and societal demands, including the needs of particular customer groups.

This paper aims to empirically investigate the role of market-based transformative service initiatives (TSIs) during the refugee crisis and shed light on how such TSIs increase inclusion of refugees in service systems by using market forces while creating broader benefits for service organizations themselves.

This paper uses the case of the World Food Program’s (WFP) Dalili smartphone application targeting Syrian refugees in the context of Lebanon. A mixed-methods approach, including in-depth interviews with the retail managers of the local supermarkets and statistical cross- and intra-regional analysis on the retailing mix elements of the local supermarkets was adopted for the empirical investigation.

The results show that the WFP’s Dalili TSI increases service inclusion of refugees by facilitating their access to the essential food services easier and at affordable prices and helps them integrate into the host community. Furthermore, such market-based TSIs were shown to have broader benefits for other stakeholders in the food retail ecosystem including retailers and nonrefugee shoppers as they are successful in improving the retailing management standards of the participating supermarkets by decreasing the average retail price of the merchandise, increasing their variety and assortment, increasing promotional offers and improving the customer service level.

This research fills the gap in the literature for empirical investigation on the impact mechanism of market-based TSIs on service inclusion and well-being of refugees. In contrast to the majority of TSIs studied in the literature that are designed by governments or nonprofit organizations in the areas such as higher education, health care and humanitarian aids, this study focuses on the case of TSIs developed by supranational organizations using market forces in the food retail ecosystem. Furthermore, the findings suggest that TSIs could also benefit the service organizations that offer such initiatives.

The findings of this paper have implications for service organizations and policymakers and their ability to design effective market-based TSIs during the refugee crisis.

The studied case in the context of TSIs in the food retail ecosystem and the empirical approach used are academically novel. Moreover, focusing on the refugee crisis in the Middle East region is rather understudied in the service research literature.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

Social media networks make their services freely available to all users. Users pay for the service received with the time and attention taken by the advertisements. This chapter argues that social media platforms are a unique form of monopoly driven by “the more the merrier” effect (i.e., network effects) in users' consumption. These monopolies exercise market power, not by charging higher prices to users but by “tying” larger amounts of advertising to their content. Traditional antitrust instruments designed to address excessive pricing and reduced output by monopolies need to be reframed to tame the attention economy problems in the social media industry. This chapter discusses five antitrust instruments grouped in three categories: structural, behavioral, and market-based remedies. Market-based solutions are the least explored in the literature, despite being the most promising instruments to lower the attention costs imposed on users, while preserving the economies of scope in production and the network effects in consumption, and possibly maintaining free access to social media, as we know it today.

Start-ups are successful in receiving valuation in billions of US dollars prior to initial public offering (IPO). However, to sustain higher valuation, the stocks need to perform consistently after the IPO. Short-run stock performance of India-based start-ups during the first year of IPO listing from March 2021 to March 2022 is analysed.

The paper deals with the new generation start-ups' stock performance in emerging market in terms of total and abnormal return generated in comparison to the market (NIFTY-200). Further, the volatility of returns during bear and bull regimes is analysed through a family of Markov-switching GARCH models using both normal and skewed distributions.

The results suggest that start-up stocks are more volatile during bear regime than in the bull run in market-based economies where price limit policy does not apply. Besides, the cumulative abnormal return over the market return was lower for majority of start-up IPO stocks.

Though negative returns of the start-up stocks during the first year of IPO need not be surprising, higher volatility during bear regime is a matter of concern as it could severely impact retail investors and founders. The results hold implication for IPO regulation in emerging markets and for retail investors desirous of investing in start-up stocks.

Volatility of return is examined using a state-space model during the first year of the start-up IPO listing. The study contributes to the emerging market IPO literature by examining IPO performance in market-based economy. Previous IPO performance studies in emerging markets are predominantly based on ecosystems where start-ups are subjected to price limit policy, and it does not reflect the true nature of IPO performance across emerging markets.

In this study, the authors examine the relationships between market-based regulations and corporate carbon disclosure and carbon performance. The authors also investigate whether these relationships vary across emission-intensive and non-emission intensive industries.

The study sample consists of the world's 500 largest companies across most major industries over a recent five-year period. Country-specific random effect multiple regression analysis is used to test empirical models that predict relationships between market-based regulations and carbon disclosure and carbon performance.

Results indicate that market-based regulations significantly and positively affect corporate carbon performance. However, market-based regulations do not significantly affect corporate carbon disclosure. This study also finds that the association between regulatory pressures and carbon disclosure and carbon performance varies across emission-intensive and non-emission-intensive industries.

The findings of this study have key implications for policymakers, practitioners and future researchers in terms of understanding the factors that drive businesses to increase their carbon performance and disclosure. The study sample consists of only large firms, and future researchers can undertake similar studies with small and medium-sized firms.

The results of this study are expected to help business managers to identify the benefits of adopting market-based regulations. Regulators can use this study’s results to evaluate if market-based regulations effectively improve corporate carbon performance and disclosure. Furthermore, stakeholders may use this study to evaluate and improve their businesses' reporting of carbon disclosure and performance.

In contrast to current literature that has used command and control regulations as a proxy for regulation, this study uses market-based regulations as a proxy for climate change regulations. In addition, this study uses a more comprehensive measure of carbon disclosure and carbon performance compared to the previous studies. It also uses global multi-sector data from carbon disclosure project (CDP) in contrast to most current studies that use national data from annual reports of sample firms of specific sectors.

This study aims to present a conceptual framework for understanding the dual orientations of market-based organizational learning (MBOL), namely, market orientation (MO) and learning orientation (LO), in the development of pricing capability (PC) with the goal of improving business performance (BP). This framework further explores the moderating effects of coordination mechanism (CM) and environmental dynamism (ED) on the PC–BP link and the mediating role of PC on the relationship between MBOL and BP.

This study applied the partial least squares structural equation modeling on survey data from 298 Indian small- and medium-sized enterprises (SMEs) operating in manufacturing and service sectors.

The findings indicate that MBOL significantly contributes to PC development, which in turn improves BP. Interestingly, PC acts as a partial mediator in the MO–BP link, as well as LO–BP link. Moreover, CM and ED strengthen the effect of PC on BP. Finally, MO and LO have substantial and distinct effects on PC and BP.

This study examines only one market-related capability, i.e. PC, considers multi-industry SMEs rather than specific large industries and uses cross-sectional instead of longitudinal data.

These findings are crucial from managerial standpoints because SMEs need to understand the MBOL dimensions, including MO and LO, and their significance in improving PC and BP.

Understanding how MBOL adoption contributes to superior performance is critical, but research in the SMEs context is still lacking. This study addresses a research gap by examining the impact of MBOL on BP, both directly and indirectly, through PC in the context of SMEs.

Rising energy demand and the quest for achieving climate change targets have been pushing emerging markets like India to bolster the natural gas share in their energy mix. The country has set an aggressive target of increasing natural gas share in the energy mix to 15% by 2030. The purpose of this study is to acknowledge the need for adopting and developing strategies for natural gas business market development to ensure a reliable supply at an affordable price. Hence, this study explores the natural gas market business development strategies and assesses them through cause/effect analysis.

This study proposed an integrated framework based on the Grey concept and Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique to assess and determine the interdependence among the natural gas business market development strategies by cause-and-effect group analysis. The application of Grey theory reduced the uncertainty and subjectivity involved in the decision-making process. Later, sensitivity analysis is also performed to check the robustness of the framework.

The natural gas business market development strategies are identified through a systematic literature search and contributions from industry experts. The findings of this study highlight the importance of developing pipeline and storage infrastructure facilities, ensuring supply security through long-term imports and overseas investment, implementing free-market-based pricing, simplification and standardization of regulatory processes at state and national levels, etc., for the development of the natural gas market development in India.

This study acknowledges the natural gas market development strategies and evaluated them into cause-and-effect groups which are limited to Indian context. All evaluations in the Grey-based DEMATEL method were made in this study based on the decision team inputs which limits the generalization to other geographies. Moreover, the opinions of the experts can be subjective and differ. The selection of the experts is done through non-probability sampling process.

This study could support the government and decision-makers in formulating the appropriate strategies to develop the domestic natural gas market. The cause-and-effect relationships are helpful for the companies, management, government, regulators and other stakeholders to understand the criticality of the causal strategies that must be implemented for developing the favorable natural gas business market scenario.

This study explores and evaluates the strategies that successfully bolster the natural gas business demand in India using Grey-based DEMATEL framework. By focusing on those critical strategies, relevant stakeholders would ensure a reliable natural gas supply at affordable prices.

Based on the attention-based view (ABV), this study examines the mechanism of external pressure and internal managerial interpretation affecting the promotion of green entrepreneurial orientation (GEO) of agricultural enterprises.

Based on data collected from 208 agricultural enterprises in China, the conceptual model was tested by using hierarchical regression.

The results show that managerial interpretation can affect the promotion of GEO. Command and control regulation, market-based regulation and green market pressure are important external pressures that affect the promotion of GEO. In addition, managerial interpretation mediates the relationship between command and control regulation and GEO, market-based regulation and GEO, as well as green market pressure and GEO.

This study proposes a key path for promoting the adoption and implementation of GEO by agricultural enterprises. The research results provide experience for emerging and developing countries to promote the GEO of agricultural enterprises, which is helpful to alleviate the environmental problems caused by the development of agricultural enterprises.

For the first time, this study introduced the ABV into the research of GEO. The research results enrich the theoretical perspective of GEO and expand the research field of the ABV. In addition, this study fills the research gap that existing research has not paid enough attention to the internal driving factors of GEO and opens the black box between the external pressure and GEO.

This chapter introduces empirical studies of firm performance and related risk outcomes conducted in the management and finance fields presenting underlying theoretical rationales as they have evolved over time. Early finance studies of market-based returns predominantly found positively skewed return distributions that conform to assumptions about higher returns associated with more risky investments. Subsequent studies found that performance outcomes measured as accounting-based financial returns generally display left-skewed distributions that reflect negative risk-return relationships. This artifact was first observed by Bowman (1980), thus often referred to as the “Bowman paradox” because it contravened the conventional assumptions in finance. The management studies have largely confirmed the inverse risk-return observations but often following rather confined research streams. A contingency perspective inspired by prospect theory and behavioral rationales have investigated the lagged effects of performance on risk outcomes and vice versa. Another stream has focused on the spurious relationships between negatively skewed performance distributions and the inverse risk-return associations. A third approach considered the performance and risk outcomes as deriving from the firms responding in distinct ways to exogenous changes. These studies reach comparable results but underpinned by very different rationales. The finance studies observe deviations from the pure doctrine of positive risk-return associations embedded in the widely adopted capital asset pricing model (CAPM) and note deficiencies with alternative interpretations that even question the validity of CAPM. A more recent strain of studies in behavioral finance observes how many (even professional) investment managers have biases that lead to inverse relationships between perceived risk and return outcomes. While these diverse fields of study have different starting points, they uncover an increasing number of interesting commonalities that can inspire the ongoing search for explanations to observed left-skewed financial returns and negative risk-return correlations across firms.