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This study examines the importance of income income-shifting arrangements of US multinational corporations (MNCs) on future stock price crash risk.

This study employs a sample of 7,641 corporation-year observations over the 2005–2017 period and uses ordinary least squares regression analysis.

The authors find that the income-shifting arrangements of MNCs are positively and significantly associated with stock price crash risk after controlling for corporate tax avoidance and other known determinants of stock price crash risk in the regression model. This result is robust to alternative measures of stock price crash risk and income-shifting, and several endogeneity tests. The authors also observe that income-shifting arrangements increase stock price crash risk both directly and indirectly through the information opacity channel. Finally, in cross-sectional analyses, the authors find that the positive association between income-shifting and stock price crash risk is more pronounced for MNCs that use tax haven subsidiaries and have weak corporate governance mechanisms.

The authors provide new empirical evidence that MNCs will likely face significant capital market consequences regarding their income-shifting arrangements.

The purpose of this paper is to have a deeper understanding of the nonlinear relationship between the impact of climate change on tourism development. Current studies on the effects of climate change on tourism development primarily rely on linear correlation assumptions.

Based on the New Institutional Economics theory, the institutional setting inherently motivates and ensures the growth of the tourism industry. For a precise evaluation of the nonlinear consequences of climate change on tourism, this paper concentrates on Chinese cities between 2011 and 2021, methodically analyzing the influence of climate change on tourism.

The study findings suggest that there is an “inverse U”-shaped nonlinear relationship between climate change and tourism development, initially strengthening and subsequently weakening. Based on these findings, the research further delves into how institutional contexts shape the nonlinear association between climate change and tourism growth. It was found that in a higher institutional backdrop, the “inverse U” curve tends to flatten and surpass the curve adjusted for a lesser institutional context. Upon deeper mechanism analysis, it was observed that cities with more advanced marketization, improved industrial restructuring and enhanced educational growth exhibit a more evident “inverse U”-shaped nonlinear connection between climate change and tourism evolution.

First, previous studies on climate change and tourism development largely rely on questionnaire data (Hu et al., 2022). In contrast to these studies, this paper uses dynamic panel data, which to some extent overcomes the subjectivity and difficulty of causality identification in questionnaire data, making our research conclusions more accurate and reliable. Second, this study breaks through the linear relationship hypothesis of previous literature regarding climate change and tourism development. By evaluating the nonlinear relationship of climate change to tourism development from the institutional pressure perspective, it more intricately delineates their interplay mechanism, expanding and supplementing the research literature on the relationship mechanism between climate change and tourism development. Thirdly, the conclusions of this study are beneficial for policymakers to better understand and assess the scope of climate change impacts. It also aids relevant departments in clarifying the direction of institutional environment optimization to elevate the level of tourism development when faced with adverse impacts brought about by climate change.

Profit shifting is a matter of great concern for governments internationally. It leads to the loss of tax revenues and puts multinational corporations (MNCs) in a disparate position. Lately, due to the aggressive stance of the Indian taxman, several Indian MNCs are planning to minimise their tax outflows. This paper aims to study profit-shifting drawing from the institutional theory for the Indian MNCs.

The sample comprises 679 MNCs listed on the Bombay Stock Exchange or the National Stock Exchange with either Indian parents with foreign subsidiaries (553) or Indian subsidiaries of a foreign parent (126) for FY 2013–14 to FY 2018–19. A fixed-effect panel regression technique was invoked to examine tax rate differential motivated profit-shifting undertaken by MNCs with the moderating effect of international presence and patents.

The results suggest that MNCs shift their profits to take advantage of differences in global tax rates when they have an international presence in at least five tax countries. Further, profit shifting is likely towards no-tax compared to low-tax countries, with the presence of patents in an MNC group having no significant impact.

Losses to the government revenue due to profit shifting by MNCs are rather severe in emerging economies. The study provides the first empirical evidence of the direction of profit shifting with the moderating effect of the extent of global presence and group patents, which would interest scholars in the field. The findings provide valuable insights to the policymakers, highlighting the urgent need to operationalise the general anti-avoidance taxation rules.

When companies face a crisis, they sometimes deliver blame-shifting communications, trying to shift blame onto another actor to protect their reputation. While previous research has considered how different features of the message affect its persuasiveness, little is known about whether specific senders can blame more effectively. This paper aims to contribute to research in this domain through an investigation of the sender’s social perception as a critical moderator to the persuasiveness of blame shifting.

The authors conduct four between-subjects scenario experiments to test the research hypotheses. In each experiment, participants are presented with a realistic crisis scenario and the crisis communications delivered by the company. The authors assess the extent to which perceptions of the sender influence the message’s ability to reduce negative word-of-mouth intentions and to increase purchase intentions.

The authors show that blame shifting is more likely to be effective when deployed by senders that are small (Study 1) or have a positive CSR track record (Study 2). Furthermore, The authors find that even large senders can successfully deploy blame shifting if they can benefit from being known for their CSR programs (Study 3). Finally, the authors show that the effect of blame shifting depends on the receiver’s level of concern about the crisis: stakeholders significantly concerned by the crisis reject blame-shifting communications (Study 4).

Further research should examine the impact of information about brand competence on blame-shifting effectiveness. Further research is also needed to explore sender effects for other defensive crisis communication strategies such as denial or the use of excuses or justifications.

The study offers critical information for marketers considering the use of defensive crisis communications strategies such as blame shifting.

The study extends the understanding of how sender effects influence blame-shifting communications. The analysis allows us to clarify why this strategy is effective for certain senders and certain receivers while, for others, it tends to backfire. Blame shifting backfires for large senders unless they can boast a strong CSR record.

The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.

This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.

Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.

Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.

This article tackles the intersection of mothering and labor through the author's own experience as a feminist mother/manager from Istanbul, Turkey. It aims to revisit the first years of motherhood, exploring the struggle to invent a peculiar maternal subjectivity in opposition and negotiation with the patriarchal institution of motherhood, the new definition of maternal labor in a highly digital, neoliberal context and the issue of marital fairness in a dual-income heterosexual marriage.

The article presents an autoethnographic, retrospective and introspective inquiry into the first seven years of the author's mothering experience in order to offer an in-depth exploration of the various aspects of contemporary maternal labor.

The article shows how maternal labor has shifted in nature and expanded in scope in a contemporary non-Western context. It investigates the dissolution of the spatial, temporal and sensorial boundaries between the managerial labor dedicated to the workplace, and to the family. Highlighting the similarities of the two forms of labor, the article manifests the materiality, tangibility and visibility of maternal labor.

Further intersectional studies shall be beneficial to redefine maternal labor in different contexts.

Departing and diverting from the terms “invisible labor” and “mental load”, the article suggests a shift in terminology to stress the multifaceted medley of managerial tasks mothers undertake today.

The article provides an original take on maternal labor through the first-hand experience of a middle-class, professional mother from Istanbul, Turkey.

The applications of Artificial Intelligence (AI) in various areas of professional and knowledge work are growing. Emotions play an important role in how users incorporate a technology into their work practices. The current study draws on work in the areas of AI-powered technologies adaptation, emotions, and the future of work, to investigate how knowledge workers feel about adopting AI in their work.

We gathered 107,111 tweets about the new AI programmer, GitHub Copilot, launched by GitHub and analysed the data in three stages. First, after cleaning and filtering the data, we applied the topic modelling method to analyse 16,130 tweets posted by 10,301 software programmers to identify the emotions they expressed. Then, we analysed the outcome topics qualitatively to understand the stimulus characteristics driving those emotions. Finally, we analysed a sample of tweets to explore how emotional responses changed over time.

We found six categories of emotions among software programmers: challenge, achievement, loss, deterrence, scepticism, and apathy. In addition, we found these emotions were driven by four stimulus characteristics: AI development, AI functionality, identity work, and AI engagement. We also examined the change in emotions over time. The results indicate that negative emotions changed to more positive emotions once software programmers redirected their attention to the AI programmer's capabilities and functionalities, and related that to their identity work.

Overall, as organisations start adopting AI-powered technologies in their software development practices, our research offers practical guidance to managers by identifying factors that can change negative emotions to positive emotions.

Our study makes a timely contribution to the discussions on AI and the future of work through the lens of emotions. In contrast to nascent discussions on the role of AI in high-skilled jobs that show knowledge workers' general ambivalence towards AI, we find knowledge workers show more positive emotions over time and as they engage more with AI. In addition, this study unveils the role of professional identity in leading to more positive emotions towards AI, as knowledge workers view such technology as a means of expanding their identity rather than as a threat to it.

When a process experiences an out-of-control condition, identification of the change point is capable of leading practitioners to an effective root cause analysis. The change point addresses the time when a special cause(s) manifests itself into the process. In the statistical process monitoring when the chart signals an out-of-control condition, the change point analysis is an important step for the root cause analysis of the process. This paper attempts to propose a model approaching the artificial neural network to identify the change point of a multistage process with cascade property in the case that the process is modeled properly by a simple linear profile.

In practice, many processes can be modeled by a functional relationship rather than a single random variable or a random vector. This approach of modeling is referred to as the profile in the statistical process control literature. In this paper, two models based on multilayer perceptron (MLP) and convolutional neural network (CNN) approaches are proposed for identifying the change point of the profile of a multistage process.

The capability of the proposed models are evaluated and compared using several numerical scenarios. The numerical analysis of the proposed neural networks indicates that the two proposed models are capable of identifying the change point in different scenarios effectively. The comparative sensitivity analysis shows that the capability of the proposed convolutional network is superior compared to MLP network.

To the best of the authors' knowledge, this is the first time that: (1) A model is proposed to identify the change point of the profile of a multistage process. (2) A convolutional neural network is modeled for identifying the change point of an out-of-control condition.

Despite the increasing interest in the role of supply chain concentration (SCC) in improving performance, its influence on firms' sustainability performance remains unexplored, as do the underlying mechanisms of this relationship. Drawing on resource dependence theory, the authors investigate the relationship between SCC and manufacturing firms' sustainability performance and the moderating roles of operational slack and information transparency.

The authors use secondary data from 3,581 manufacturing firms listed on the Shanghai and Shenzhen A-share stock markets from 2006 to 2020 to conduct an empirical analysis using panel data regression models.

Manufacturing firms' SCC is negatively related to sustainability performance until it reaches a certain point, where SCC positively affects sustainability performance, presenting a U-shaped relationship. In addition, operational slack represented by a quick ratio moderates the relationship between SCC and sustainability performance by flattening the curve. Operational slack represented by receivable turnover ratio moderates the relationship between SCC and sustainability performance by steepening the curve and shifting the turning point left. Information transparency strengthens the effect of SCC on the sustainability performance by steepening the curve.

This investigation provides a comprehensive view of the SCC– sustainability performance relationship.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.