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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。

This study aims to meticulously evaluate the public service value-generation process facilitated by collaborative e-governance services within the framework of the National e-governance Plan (NeGP).

The study formulates a comprehensive research model through a combination of literature review, insights from domain experts and hands-on experience gained from the e-governance project. A conceptual research model was meticulously structured, validated, and interpreted by using a reflective measurement theory. The analytical tool SmartPLS3 was used to assess the proposed model rigorously.

The analysis of collected data reveals a statistically significant positive correlation between the implementation of collaborative e-governance strategies and the creation of public service value. This relationship is further reinforced by a strong alignment between the perceived aspects of collaborative e-governance, such as responsiveness, transparency and service delivery and their substantial contribution to the enhancement of public service value.

This study contributes to the scholarly discourse by introducing an innovative methodology for assessing public service value through analyzing empirical data from citizen-centric collaborative e-governance projects. It is noteworthy that no prior studies have examined the nuanced concept of public service value in the context of collaborative e-governance.

Online labour markets (OLMs) have recently become a widespread phenomenon of digital work. While the implications of OLMs on worker well-being are hotly debated, little empirical research examines the impact of such work on individuals. The highly competitive and fast-paced nature of OLMs compels workers to multitask and to perform intense technology-enabled work, which can potentially enhance technostress. This paper examines the antecedents and well-being consequences of technostress arising from work in OLMs.

The authors draw from person–environment fit theory and job characteristics theory and test a research model of the antecedents and consequences of worker technostress in OLMs. Data were gathered from 366 workers in a popular OLM through a large-scale online survey. Structural equation modelling was used to evaluate the research model.

The findings extend existing research by validating the relationships between specific OLM characteristics and strain. Contrary to previous literature, the results indicate a link between technology complexity and work overload in OLMs. Furthermore, in OLMs, feedback is positively associated with work overload and job insecurity, while strain directly influences workers' negative affective well-being and discontinuous intention.

This study contributes to technostress literature by developing and testing a research model relevant to a new form of work conducted through OLMs. The authors expand the current research on technostress by integrating job characteristics as new antecedents to technostress and demonstrating its impact on different types of subjective well-being and discontinuous intention. In addition, while examining the impact of technostressors on outcomes, the authors consider their impact at the individual level (disaggregated approach) to capture the subtlety involved in understanding technostressors' unique relationships with outcomes.

The role of procurement managers is crucial for diffusing sustainability throughout the supply chain. Whether or not they are willing to pay for sustainability is an important and not yet fully understood question. The authors examine antecedents and consequences of their willingness to pay (WTP) for sustainability.

The authors develop a multi-level framework to examine the WTP for sustainability in a B2B context. The authors test this multi-level framework with 372 procurement managers from multiple sectors and countries using partial least squares structural equation modeling.

The authors find that individual values of procurement managers and institutional pressures directly, while ethical organizational culture indirectly influence WTP for sustainability. Functional and cognitive competencies of procurement managers improve the sustainability of procurement, but not WTP for sustainability. Importantly, WTP for sustainability directly influences the performance of the procurement function which in turn is positively associated with increased organizational performance.

The study, examining the interplay between individual, organizational and contextual factors, provides empirical evidence on the pivotal role of procurement managers in diffusing sustainability throughout the supply chain. The findings of the study, on the one hand, contribute to the literature on operations management and sustainability, and on the other hand, guide policy and managerial actions.

This study aims to analyse the influence of perceived corporate social responsibility (CSR) on purchase intention; this study also examines the mediating effect of generation Y’s attitude towards the brand and the moderating effect of their attitude towards CSR.

This study tested the model with a sample of 392 generation Y consumers using Smart partial least squares (PLS)-structural equation modelling.

Brand attitude partially mediates the positive influence of perceived CSR (PCSR) on purchase intention. Gen Y’s attitude towards CSR increases the impact of PCSR on brand attitude and purchase intention.

To multiply the effects of CSR and brand attitude, retail marketing managers can develop strategies that strengthen the links between awareness, knowledge, brand affection and purchase intent by encouraging Gen Y consumers to engage with the brand’s CSR strategy.

This study advances the literature on CSR and consumer behaviour by providing an integrated view of the hierarchy of effects model and a generational cohort perspective in predicting purchase intention.

sponsabilidad social corporativa y la actitud hacia la marca en la intención de compra

el estudio analiza la influencia de la responsabilidad social corporativa (RSC) percibida sobre la intención de compra. De igual forma, se analizan el efecto mediador de la actitud hacia la marca y el efecto moderador de la actitud hacia la RSC de la Generación Y.

el modelo se contrastó con una muestra de 392 consumidores de la generación Y utilizando SMART PLS-SEM.

la actitud hacia la marca media parcialmente la influencia positiva entre la RSC percibida y la intención de compra. La actitud de la Gen Y hacia la RSC multiplica el impacto de la RSC percibida sobre la actitud hacia la marca y sobre la intención de compra.

con la finalidad de multiplicar los efectos de la RSC y de la actitud hacia la marca, los directivos del marketing minorista pueden desarrollar estrategias que refuercen los vínculos entre concienciación, conocimiento, afecto por la marca e intención de compra fomentando la implicación de los consumidores de la generación Y con la estrategia de RSC de la marca.

El estudio avanza en la literatura sobre RSC y comportamiento del consumidor al ofrecer una perspectiva integrada del modelo de jerarquía de efectos (HOE) y la perspectiva de cohortes generacionales en la predicción de la intención de compra.

本研究分析了感知到的企业社会责任对购买意向的影响。同样, 我们也分析了Y世代的品牌态度的中介效应和企业社会责任态度的调节效应。

使用SMART PLS-SEM对392名Y世代消费者的样本进行了模型测试。

品牌态度部分调解了感知的企业社会责任和购买意向之间的积极影响。Y一代对企业社会责任的态度使感知到的企业社会责任对品牌态度和购买意向的影响倍增。

为了使企业社会责任和品牌态度的效果倍增, 零售业营销人员可以制定战略, 通过鼓励Y一代消费者参与品牌的企业社会责任战略, 加强意识、知识、品牌喜爱和购买意向之间的联系。

This study investigated the concerns and plans of construction professionals about building information modeling (BIM) implementation, found the acceptable BIM implementation driving forces and strategies for them and developed a prescriptive BIM implementation model to help understand how BIM implementation concerns, intentions, driving forces and strategies are connected.

This study employs a positivist paradigm with a hypothetico-deductive research strategy as well as concern-based adoption theory as a conceptual lens to distinguish construction professionals (CPs)' BIM implementation concerns and intentions. This implies that the forces driving BIM implementation intentions and concerns are related to BIM implementation methods and that their concentrations are proportional to the intensity of BIM implementation strategies. A 16-item questionnaire tailored to the operations of CPs was used for data collection. The data collected from respondents were utilized to evaluate the proposed model using structural equation modeling (SEM) techniques.

Findings from the data collected from the respondents revealed that CPs are concerned about the impact of BIM deployment on their time and service quality. Their main purpose was to take drives to learn more about BIM in order to pique their curiosity. Embracing the latest digital technology and beginning self-initiated BIM training are two strategies that would be quite effective in boosting BIM deployment.

The study identifies promising directions for future BIM implementation research and development. The study's findings imply that more theoretically motivated research, rather than just empirical research, is required to refine BIM implementation concerns.

The study has implications for the professional development of CPs as well as understanding the process of implementing BIM change. The study's findings will help to understand the resource system for assessing CPs' needs and concerns and selecting personalized BIM implementation strategies.

Before this study, BIM-related studies had ignored the concerns and goals of the CPs when it came to implementing BIM. Using the CPs' concerns and hopes for BIM implementation, a systemic BIM implementation model was developed that would help and speed up BIM adoption.

To determine how to best deploy the Triple-A supply chain (SC) capabilities (AAA-agility, adaptability and alignment) to improve competitive advantage (CA) by identifying the Triple-A SC model with the highest CA predictive capability.

Assessment of in-sample and out-of-sample predictive capacity of Triple-A-CA models (considering AAA as individual constructs) to find which has the highest CA predictive capacity. BIC, BIC-Akaike weights and PLSpredict are used in a multi-country, multi-informant, multi-sector 304 plant sample.

Greater direct relationship model (DRM) in-sample and out-of-sample CA predictive capacity suggests DRM's greater likelihood of achieving a higher CA predictive capacity than mediated relationship model (MRM). So, DRM can be considered a benchmark for research/practice and the Triple-A SC capabilities as independent levers of performance/CA.

DRM emerges as a reference for analysing how to trigger the three Triple-A SC levers for better performance/CA predictive capacity. Therefore, MRM proposals should be compared to DRM to determine whether their performance is significantly better considering the study's aim.

Results with our sample justify how managers can suitably deploy the Triple-A SC capabilities to improve CA by implementing AAA as independent levers. Single capability deployment does not require levels to be reached in others.

First research considering Triple-A SC capability deployment to better improve performance/CA focusing on model's predictive capability (essential for decision-making), further highlighting the lack of theory and contrasted models for Lee's Triple-A framework.

Drawing on resource and capability theory, this study aimed to analyze the relationship between the dynamic capabilities (DC), the knowledge management (KM) process (KMP) and results in customers and people. More specifically, the study argues that the KM process mediates the relationship between DC and the results outlined above. In addition, a predictive analysis is carried out that demonstrates the relevance of the KM process in the model.

The study sample is made up of 118 Spanish organizations that have some kind of recognition of excellence awarded by the European Foundation for Quality Management (EFQM). Partial least squares methodology is used to validate the research model, the hypothesis testing and the predictive analysis.

The results show that organizations which leverage the DC through the KMP improve customer and people outcomes. Moreover, the predictive power is higher when the KMPmediates the relationship between the DC and the results.

There is no consensus in the literature on the relationship between DC, KM and performance. Moreover, there are also not enough papers that study KM or DC through the dimensions that define these constructs or variables. Given this need, this work considers the KMP according to the stages of knowledge creation, storage, transfer and application. Similarly, DC is dimensioned in sensing, learning, integrating and coordinating capabilities. These, as reconfigurators of knowledge assets, influence the KMP. Accordingly, the empirical model connects these knowledge domains and analyses their link to outcomes.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.