Search results

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.

The authors analysed fairness as an antecedent of trust to understand how trust building can be actively managed to facilitate collaboration in buyer–supplier relationships.

A case study was employed to buyer–supplier relationships of five organic food companies from three different sectors. The research was developed with data collected in 2022 through qualitative research interviews and analysed using a structured content analysis. A practitioner workshop served to complement findings and confirm the interpretation of data by discussing interview results from a managerial perspective.

A conceptual model based on organisational trust and justice theory was developed to examine causal relations between fairness and trust. Findings show that perceived distributional, procedural and interactional fairness mutually interact with the perceived trustworthiness of business partners and that both contribute to building personal, organisational and institutional trust. Qualitative data support the conceptual model and show that trust is a valuable relational resource that affects relationship quality and the willingness to collaborate and to take risks in times of uncertainty.

High trust levels developed through positive fairness perceptions can lower risk perceptions whilst increasing the willingness to collaborate in supply chain relationships. This can help deal with market uncertainties.

Through the lens of organisational trust and justice theory, this study extends the literature on collaboration in supply chains by including a comprehensive view on the formation of trust in business-to-business (B2B) relationships.

Under the pressure of dynamic business environments, firms in the banking and finance industry are gradually embracing Fintech, such as robo-advisors, as part of their digital transformation process. While robo-advisory services are expected to witness lucrative growth, challenges persist in the current landscape where most consumers are unready to adopt and even resist the new service. The study aims to investigate resistance to robo-advisors through the privacy and justice perspective. The human-like attributes are modeled as the antecedents to perceived justice, followed by the subsequent outcomes of privacy concerns, perceived intrusiveness and resistance.

An online survey was conducted to gather consumer responses about their perceptions of robo-advisors. Two hundred valid questionnaires were collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).

The results revealed that (1) perceived anthropomorphism and perceived autonomy are the positive determinants of perceived justice, (2) perceived justice negatively impacts privacy concerns and perceived intrusiveness and (3) privacy concerns and perceived intrusiveness positively influence resistance to robo-advisors.

The present study contributes to robo-advisory service research by applying a privacy and justice perspective to explain consumer resistance to robo-advisors, thereby complementing past studies that focused on the technology acceptance paradigm. The study also offers practical implications for mitigating resistance to robo-advisors.

The study analysed the influence of organizational justice on the duty orientation of employees in the mining sector of Ghana. Also, it examined the mediating role of supervisor-provided resources in the relationship between organizational justice and duty orientation.

The study obtained data through a self-administered questionnaire from 291 employees of a mining firm. The data were analysed and interpreted in light of the hypotheses using the partial least squares structural equation modelling technique.

The findings revealed that organizational justice had a significant positive relationship with duty orientation and supervisor-provided resources. The results again established that supervisor-provided resources had a significant positive relationship with duty orientation. The study finally documented that supervisor-provided resources partially mediate the relationship between organizational justice and duty orientation.

We recommended that the management of the mining companies devote resources to developing organizational justice policies based on fairness in resource allocation, clear roles, employee feedback and effective information dissemination. Furthermore, supervisors should place priority on acquiring and dispensing resources as employees demonstrate their willingness to improve duty orientation.

The study contributes to knowledge in a novel research area. It adds to empirical evidence by highlighting the possible variables that may influence employees to engage in duty orientation.

本研究擬分析於迦納的採礦部門裏,組織公平感對僱員職責導向的影響;研究亦擬探討主管提供的資源,如何在組織公平感與職責導向間的關係上起著中介角色。

研究人員透過一間採礦公司291名僱員自我測試的問卷,取得研究所需的數據,繼而以偏最小平方結構方程式模式分析法,進行數據分析,並按照研究的假設,對數據進行闡釋的工作。

研究結果顯示,組織公平感與職責導向和主管提供的資源之間存在顯著的正向關係;研究結果亦確定了主管提供的資源與職責導向之間存在顯著的正向關係。最後,研究結果證明了主管提供的資源,會一定程度調節組織公平感與職責導向之間的關係。

我們建議採礦企業的管理層應根據資源的公平分配、明確的角色、僱員的回饋和有效的信息傳播,把資源專用於發展組織公平感的政策上;而且,當僱員展示他們願意改善職責導向時,主管應把獲取資源,並加以發放列為優先事項。

本研究在一個新穎的研究領域裏,幫助我們增進知識;研究透過強調影響僱員參與職責導向的可能變數,增加有關的經驗證據。

This paper aims to investigate how user expectations affect value-added service (VAS) investment and pricing decisions of two-sided platforms. It draws on the information asymmetry theory and offers suggestions on how platform operators can manage user expectations.

According to the game theory, this study considers three user expectations (responsive, passive and wary). By framing the Hotelling duopoly model and comparing the VAS investment, price and platform profits, the optimal platform decision is analyzed and discussed.

The conclusions demonstrate that the monopolistic two-sided platform obtains more profits from the informed users with responsive expectations than uninformed users with passive or wary expectations. The marginal investment cost and cross-network externalities are two key factors that determine the platform's VAS investment and pricing strategies of passive or wary users. Furthermore, considering the expectation preferences, i.e. the uniformed users hold wary expectations with more information and hold passive expectations with less or no information, the results suggest that the proportion of wary users to all uninformed users increases the platform's VAS investment, profits and the price of informed users, and increase (decrease) the price of uninformed users when the cross-network externalities of informed users are relatively small (larger).

These results can provide insightful enlightenment into how platform operators utilize bilateral users' expectations and information level to guide their VAS investment and pricing decisions.

This paper is one of the first to explore the impact of three user expectations and the heterogeneity of preferences in informing users' passive or wary expectations, based on different levels of information on the decision-making of two-sided platforms regarding VAS.

Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area.

The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data.

Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior.

The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain.

La Inteligencia Artificial (IA) ofrece muchos beneficios para mejorar la práctica del marketing predictivo. Sin embargo, plantea preocupaciones éticas relacionadas con la priorización de clientes, la concentración de cuota de mercado y la manipulación del consumidor. Este artículo explora estas preocupaciones éticas desde una perspectiva contemporánea, basándose en las experiencias y perspectivas de profesionales en IA y marketing predictivo. El estudio tiene como objetivo contribuir a la literatura de este ámbito al proporcionar una perspectiva moderna sobre las preocupaciones éticas del uso de la IA en el marketing predictivo, basándose en las experiencias y perspectivas de profesionales en el área.

Para realizar el estudio se realizaron entrevistas semiestructuradas durante seis semanas con 14 participantes con experiencia en sistemas habilitados para IA en marketing, utilizando técnicas de muestreo intencional y de bola de nieve. Se utilizó un análisis temático para explorar los temas que surgieron de los datos.

Los resultados revelan que el uso de la IA en marketing podría tener consecuencias no deseadas, como perpetuar sesgos existentes, violar la privacidad del cliente, limitar la competencia y manipular el comportamiento del consumidor.

El estudio identifica siete temas y los comparan con el modelo de Ashok para proporcionar una perspectiva estructurada para interpretar los resultados. El marco presentado por esta investigación es único y puede utilizarse para respaldar investigaciones éticas que abarquen aspectos sociales, tecnológicos y económicos dentro del ámbito del marketing predictivo.

人工智能（AI）为改进预测营销实践带来了诸多益处。然而, 这也引发了与客户优先级、市场份额集中和消费者操纵等伦理问题相关的观点。本文从当代角度深入探讨了这些伦理观点, 充分借鉴了人工智能和预测营销领域专业人士的经验和观点。旨在通过现代视角提供关于在预测营销中应用人工智能时所涉及的伦理观点, 为该领域做出有益贡献。

本研究采用了目的性和雪球抽样技术, 与14位在人工智能营销系统领域具有丰富经验的参与者进行为期六周的半结构化访谈。研究采用主题分析方法, 旨在深入挖掘数据中显现的主要主题。

研究结果表明, 在营销领域使用人工智能可能引发一系列意外后果, 包括但不限于加强现有偏见、侵犯客户隐私、限制竞争以及操纵消费者行为。

本研究通过明确定义七个独特的主题, 并采用阿肖克模型进行基准比较, 为读者提供了一个结构化的视角, 以解释研究结果。所提出的框架具有独特之处, 可有效支持在跨足社会、技术和经济领域的预测营销中展开的伦理研究。

A workplace bullying dynamic involving multiple individuals targeting victims can lead to the victim losing emotional bonds or affect-based trust with their colleagues, resulting in employee silence. The literature has largely ignored this negative aspect of social dynamics. This study aims to examine the relationship between workplace bullying and employee silence behaviors and determine whether affect-based trust mediates this relationship and whether climate for conflict management moderates the mediated relationship.

Hypotheses are tested using surveys and scenario-based experiments among faculty members in Indian Universities. There were 597 participants in the survey and 166 in the scenario-based experiment.

Results revealed that workplace bullying correlated positively with silence behaviors, and affect-based trust mediated the bullying-silence relationship. The hypothesized moderated mediation condition was partially supported as moderated the mediating pathway, i.e. indirect effects of workplace bullying on defensive silence and ineffectual silence via affect-based trust were weaker for employees with high climate for conflict management. However, the study failed to support the moderation of climate for conflict management in the relationship between workplace bullying and affect-based trust and workplace bullying and relational silence. The results of this moderated effect of climate for conflict management were similar in both studies.

This study is one of the few attempts to examine employee silence in response to workplace bullying in academia. Additionally, the study revealed a critical area of trust depletion associated with bullying and the importance of employee perceptions of fairness toward their institutions’ dispute resolution processes.

The paper explores the notion of worker voice in terms of its implications for supply chain justice. The paper proposes the value of the recognition perspective on social justice for framing workers’ experiences in global supply chains and identifies opportunities for the advancement of the worker voice agenda with recognition justice in mind.

The paper adopts a conceptual approach to explore the notion of worker voice in supply chains in terms of the recognition perspective on social justice.

Sustainable supply chain management (SSCM) scholarship has considered worker voice in terms of two key paradigms, which we term communication and representation. To address recognition justice for workers in global supply chains, the worker voice agenda must consider designing worker voice mechanisms to close recognition gaps for workers with marginalised identities; the shared responsibilities of supply chain actors to listen alongside the expectation of workers to use their voice; and the expansion of the concept of worker voice to cut across home-work boundaries.

The paper offers conceptual clarity on the emerging notion of worker voice in SSCM and is the first to interrogate the implications of recognition justice for the emergent worker voice agenda. It articulates key opportunities for future research to further operationalise worker voice upon a recognition foundation.