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The design of effective policies that increase access to agricultural credit should consider understanding credit constraint farmers’ groups and their response to changes in the credit conditions. To contribute to this understanding, this study surveyed farmers from Chile and classified them into five credit constraint categories discussed in credit literature. In addition, these farmers indicated how they would react to a series of hypothetical conditions related to changing interest rates, loan maturity and grace periods. Their responses were employed to measure credit demand scores (i.e. relative elasticities). Regression tests evaluated how different types of farmers reacted to changing credit conditions.

Farmers from Chile were surveyed using a mix of random and convenience sampling. Surveyed farmers were classified into five credit constraint categories proposed by previous research. Farmers rated their demand for credit on a five-point Likert-type scale for hypothetical changes in interest rates, loan maturities and grace periods. Their responses were employed to measure credit demand scores or relative credit elasticities. The study evaluated credit elasticity as a function of farmers’ credit constraint and some control variables using several regressions, including OLS, ordered probit and hierarchical regression.

The study identified 44% unconstrained nonborrowing farmers, 23% unconstrained borrowers, 14% quantity-constrained, 16% risk-constrained and 3% transaction cost-constrained farmers. Unconstrained borrowers and quantity-constrained farmers responded most to changing interest rates and loan maturity conditions. In addition, unconstrained nonborrowers and risk-constrained farmers were statistically less sensitive to changes in credit conditions than unconstrained borrowers. This finding is significant because, as discussed, unconstrained nonborrowers represent 44% of our sample. Furthermore, risk-constrained farmers were the least sensitive to changes in interest rates and loan maturity across all other credit categories.

This study gives insights that can guide agribusiness policies to enhance access to credit in developing countries such as Chile. Agricultural credit capital institutions can better target their clientele by identifying farmers’ possible reactions before implementing policy changes to increase access to credit. This study’s credit constraint categorization and the results discussed can guide that identification. For instance, policies directed toward unconstrained borrowing farmers may find positive responses. However, implementing policies targeting the other three groups (unconstrained nonborrowing, risk-constrained and transaction cost-constrained farmers) is more challenging because these farmers are less responsive to changing credit conditions.

This article correlates farmers’ propensity to borrow and credit constraints across five categories of farmers. Prior research using this categorization framework has not identified farmers into the five groups. Furthermore, in addition to interest rate and loan maturity credit demand relative elasticity, this study adds the grace period elasticity, which has not been included in previous studies on agricultural credit.

The United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) aims to transform the lives of people with disabilities around the globe. Many challenges to achieving that goal still remain. The area of disability research ethics is one of those challenges and is the subject of this chapter. Systemic reform of disability research ethics is needed in order to ensure that the work of the UNCRPD rests on a bedrock of quality research and data collection. In that way, progress can be supported and any regression of disabled people’s human rights and equality can be recognised and reversed (Good, 2020). While much work has already been done, inconsistencies remain with regard to the fundamental challenge of removing all ableism from the UNCRPD knowledge base. The COVID-19 pandemic of 2020–2023 starkly revealed the level of remaining ableism across the world, and how this ableism meant that Covid negatively impacted the lives of disabled people more extremely than others. This was revealed, for example, in a recent study of Covid era policies in 14 countries (Shikako et al., 2023) and also in a study of disabled people’s experiences of the Covid pandemic in South Africa (Wickenden et al., 2023). Reformed ethics in research and data collection are needed to expose and understand the problems in policy and practice, during the pandemic, which in some cases reverted to eugenics, and to investigate how to address these. This chapter maps out some possible ways forward in the work to improve human rights and equality-based research as an ethical issue.

As women’s position in the economy and society is often explained by cultural factors, this study aims to verify whether the observed changes in female empowerment in the region of Central and East European (CEE) countries of the European Union (EU) are associated with masculinity as a cultural trait.

The authors apply the k-means clustering method to group CEE countries into clusters with similar levels of female empowerment in two time points – 2013 and 2019. Next, the authors examine the clusters and cross-reference them with the national culture’s masculinity to explore the interrelations between female empowerment and cultural traits in the CEE countries and their development in time.

The analyses reveal that female empowerment is not uniform or stable across the CEE countries. The masculinity level is not strongly related to women’s position in these countries, and changes in female empowerment are not closely linked to masculinity.

Despite the tumultuous history of women’s empowerment in the CEE countries, the issues related to gender equality and cultural traits pertaining to the region are relatively understudied in the literature. By focusing on the CEE region, the authors fill the gap in examining the independencies between female empowerment and cultural masculinity.

The purpose of this study is to examine the acceptance of artificial intelligence devices (AIDs) by customers in banking service encounters using the Artificially Intelligent Device Use Acceptance (AIDUA) model and thus test the validity of the AIDUA model in the context of the banking sector as well as extending the AIDUA model by incorporating two moderator variables, namely technology anxiety and risk aversion by regarding the nature of banking services, which are considered highly risky and technology-intensive.

About 575 valid face-to-face self-administered surveys were gathered using convenience sampling among real bank customers in Turkey. The structural equation modelling was used to test hypotheses involving both direct and moderation effects.

The current study has demonstrated that the AIDUA model is valid and reliable for the acceptance of AIDs in banking service encounters by modifying it. The study results have shown that the acceptance process of AIDs for bank customers consists of three phases. Furthermore, the study’s findings have demonstrated that technology anxiety and risk aversion have adverse moderation effects on the relationship between performance expectancy and emotion as well as on the relationship between emotion and willingness to accept AIDs, respectively.

The current study validates the AIDUA model for the banking industry. In addition, the present study is unique compared to other studies conducted in the literature since it applies the AIDUA model to the setting of banking services for the first time by considering the potential effects of two moderators.

With the increasing use of Internet and mobile handsets, mobile-based electronic payment apps (MEPAs) are becoming very popular around the world due to having various advantages. The intention of this exploratory study is to investigate the role/impact of selected factors in adopting three MEPAs of China: Wechat, Alipay and UnionPay based on the extended technology acceptance model (ETAM). After conducting a thorough and careful literature review, this study identified and divided seven such factors into three broad categories: (1) technological factors: perceived ease of use (PEU) and perceived usefulness (PU); (2) personal factors: perceived trust (PT), perceived privacy (PP) and perceived risk (PR); and (3) social factors: social influence (SI) and peer influence (PI) that were assumed to affect the intention to adopt MEPAs (IAMEPAs).

1,597 Chinese individuals were selected through purposive sampling technique who regularly used MEPAs at the time of collecting data. For collecting primary data from the selected respondents, a cross-sectional survey instrument was used. The study utilized IBM SPSS 25 for descriptive statistics and a second generation covariance-based structural equation modeling (CB-SEM) technique through AMOS 25 for testing the hypothesized relationships.

From statistical analysis, it was identified that five factors: PEU, PU, PT, SI and PI have significant positive impact on the dependent variable, IAMEPAs while PR and PP have insignificant influence on IAMEPAs.

This is one of the studies ever conducted to discover the factors that can have impact on the adoption of MEPAs using ETAM. It is strongly expected that this exploratory study can motivate the scholars to commence additional investigations regarding this increasingly popular financial technology (Fin-Tech). In addition, it can be anticipated that the MEPA service providers can widen their service effectiveness according to the users’ opinion reflected in this study. Furthermore, policymakers involved with Fin-Tech can also formulate and implement effective policies and guidelines based on the empirical outcomes.

This research considers new and unexplored explanations of why consumers continue to engage in environmentally damaging, fast fashion consumption. It explains why rational arguments alone do not prevent fast fashion consumption or encourage consumers to move toward greater adoption of sustainable fashion consumption behaviours.

This research compared the effects of a “neutral” control and an “intrasexual rivalry” experimental condition on: (1) likelihood to buy, and (2) willingness to pay, of frequent female fast fashion shoppers (N = 184).

Women use fast fashion as a conspicuous signal to other women, although this is not necessarily why they waste fast fashion purchases. Mating motives appear to produce a significant increase in fast fashion buying behaviour with women feeling intrasexual pressure to engage in consumption, and utilising consumption themselves as a self-promotion strategy.

Retailers tackling wasteful fast fashion consumption can demonstrate that sustainable consumption provides a superior conspicuous signal to fast fashion consumption, instead of solely using rational messaging.

Grounded in evolutionary psychology, this study uses three theories of intrasexual rivalry, conspicuous consumption and conspicuous waste to understand how both the volume and variety of fast fashion consumed are used as conspicuous signals in a mate attraction context.

In public management research, the focus in the public value debate has been on public administration organizations’ broader societal outcomes. Public value describes how public administrations form a vital part of the social context in which people develop and grow. However, there has not yet been an analysis of how public administration contributes to happiness in society.

In this study, we empirically analyze the relationship between people’s happiness and the public value of public administration. Our approach is based on a unique Swiss survey dataset comprising 870 individuals.

We find a positive relationship between public administration’s public value and happiness. We also find preliminary evidence with a moderation analysis that the relationship between a value-creating public administration sector and self-reported happiness is stronger for public administration employees.

While correlation studies cannot claim causal explanations and common method bias may additionally limit any research in social science, we took a number of measures to mitigate related problem. We tested our model in two samples and took both several procedural techniques and a survey design minimizing common method bias.

The paper discusses implications for public sector performance measurement for public management and practitioners.

This study calls for a more positive view on the multiple functions public administration performs for society. After an era of critical voices, our study helps reclaim public administration as a positive force for society at large in times of grand challenges, such as climate crisis, demographics and digitization.

This study has highlighted the importance between public administration’s public value and happiness in Swiss public service organizations. The study also showed that an employment in the public administration contributes to the happiness of individuals and beyond to society.

Police procedural justice is essential in shaping police legitimacy and public willingness to cooperate, yet factors that affect police fair treatment of citizens are not fully understood. Using the data of the National Police Research Platform (NPRP), Phase II, this study examines the effects of three key organizational factors (i.e. effective leadership, supervisory justice and department process fairness) on officers’ procedural justice in police stops.

Innovatively, this study links police data with citizens’ data and conducts multilevel analyses on the effects of a host of citizen, officer, incident, and, importantly, agency characteristics on officer behaviors during over 5,000 police stops nested within 48 police agencies.

The results showed that the fairness of the departmental process had a positive effect on officer procedural justice, while the fairness of the supervisor was inversely associated with procedural justice on the street.

The linked data demonstrated that organizational fairness affected street procedure justice.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

In this study, the authors develop a path model and investigate the effect of pandemic-oriented customer mistreatment on service sabotage through the lens of self-presentation theory. Moreover, the authors question the role of service climate as a moderator of the relationship between service sabotage and service performance.

Data were collected via a survey of 165 F&B frontline employees in restaurants in Iran. The hypotheses are examined using confirmatory factor analysis, structural equation modeling and ordinary least squares regression.

The findings reveal that POCM has a substantial and positive effect on service sabotage, and service climate mitigates the effect of service sabotage on service performance.

The study introduces and conceptually defines the term POCM. Furthermore, the authors apply the self-presentation theory as the overarching theory to explain underlying conditions in customer mistreatment and service sabotage. Moreover, although prior literature has described the saboteur–customer relationship as a one-line interaction, this study contributes to employee sabotage as a multi-linear transaction.

In this study, the authors identify new perspectives on the dark side of hospitality services in crises, such as the COVID-19 pandemic. The authors argue that pandemic-induced changes are essential not simply because they change customers’ moods and lower their patience threshold, but they further provoke ostentatious behaviors in saboteur–customer relations. These findings shed new light on the literature and provide managerial implications for enhancing hospitality performance.