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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。

The present study aims to examine the scope and contribution of behavioural investigative advice (BIA) reports from the National Crime Agency (NCA).

The 77 BIA reports reviewed were written between 2016 and 2021. They were evaluated using Toulmin’s (1958) strategy for structuring pertinent arguments, current compliance with professional standards, the grounds and backing provided for the claims made and the potential utility of the recommendations provided.

Consistent with previous research, most of the reports involved murder and sexual offences. The BIA reports met professional standards with extremely high frequency. The 77 reports contained a total of 1,308 claims of which 99% were based on stated grounds. A warrant and/or backing was provided for 73% of the claims. Most of the claims in the BIA reports involved a behavioural evaluation of the crime scene and offender characteristics. The potential utility of the reports was judged to be 95% for informative behavioural crime scene analysis and 40% for potential new lines of enquiry.

The reports should serve as a model for the work of behavioural investigative advisers internationally.

To the best of the authors’ knowledge, this is the first study to systematically evaluate BIA reports commissioned by the NCA; it adds to previous similar studies by evaluating the largest number of BIA reports ever reviewed, and uniquely provides judgement of overall utility.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

The purpose of this paper is to advance the understanding of paradoxes, underlying tensions and potential management strategies when integrating digital technologies into existing lean-based production systems (LPSs), with the aim of achieving synergies and fostering the development of production systems.

This study adopts a collaborative management research (CMR) approach to identify patterns of organisational tensions and paradoxes and explore management strategies to overcome them. The data were collected through interviews and focus group interviews with experts on lean and/or digital technologies from the companies, from documents and from workshops with the in-case researchers.

The findings of this paper provide insights into the salient organisational paradoxes embraced in the integration of digital technologies in LPS by identifying different aspects of the performing, organising, learning and belonging paradoxes. Furthermore, the findings demonstrate the intricacies and relatedness between different paradoxes and their resolutions, and more specifically, how a resolution strategy adopted to manage one paradox might unintentionally generate new tensions. This, in turn, calls for either re-contextualising actions to counteract the drift or the adoption of new resolution strategies.

This paper adds perspective to operations management (OM) research through the use of paradox theory, and we (1) provide a fine-grained perspective on why integration sometimes “fails” and label the forces of internal drift as mechanisms of imbalances and (2) provide detailed insights into how different management and resolution strategies are adopted, especially by identifying re-contextualising actions as a key to rebalancing organisational paradoxes in favour of the integration of digital technologies in LPSs.

This paper aims to share how the Centre for Ageing and Dementia Research co-designs research within a national programme of work to improve the lives of older adults and those affected by dementia. Through examples of this work, the authors identify the barriers and enablers to participatory approaches and lessons to inform future involvement activities.

This study reflects on implementing the UK National Standards for Public Involvement into practice. Of international relevance, the observations span the research process from research prioritisation and design to research implementation and knowledge exchange.

This study demonstrates the importance of using a relational approach, working toward a common purpose and engaging in meaningful dialogue. Only through offering choice and flexibility and actively learning from one another can co-design lead to synergistic relationships that benefit everyone.

Key implications for researchers engaged in patient and public involvement are be receptive to other people’s views and acknowledge expertise of those with lived experience alongside those with academic expertise. Training, resources and time are required to effectively support involvement and meaningful relationships. A nominated contact person enables trust and mutual understanding to develop. This is an ongoing collective learning experience that should be embedded throughout the entire research process.

This paper demonstrates how the standards are implemented with people who are often excluded from research to influence a national programme of work.

This paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.

The case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.

To improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.

Methodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

Social initiatives must disclose their results to access support. However, there is no theoretical consensus about how to do it. It is still necessary to understand the value creation in social initiatives because they may or not have economic goals. However, these goals serve to make the social ones feasible. This study aims to cut this Gordian knot by providing measures aligned to the value theory but developed by the social lens. It offers a non-economic-focused approach to dealing with assessment complexity and with multiple stakeholders’ needs for information.

A consolidation research path is suggested by three composed measures built upon tested and reliable scales. These value measures are discussed through narratives from Portuguese investors and social entrepreneurs in a mixed-methods design. Content analysis and online survey provided data for descriptive statistics, exploratory factor analysis and Cronbach's alpha test.

The analyses supported the value measures. Thus, they allow an effective way to assess and report the social value created. It also highlighted a potential use in preventive and corrective approaches helpful for several organizations that pursue social goals.

The measures were tested based on social entrepreneurs' opinions. Future studies can include beneficiaries' opinions, allowing comparisons that can help to set more realistic goals and better investment criteria.

The relationship between investors and initiatives can improve, boosting their impact on society. The measures can highlight prioritization choices that influence the way value is created. Hence, they serve as a sensemaking from a holistic standpoint.

COVID-19 has immensely disrupted business dynamism, providing catalyst innovation opportunities and transposing society's perception of disruptive technology (DT). This research increases the understanding of the impact of the pandemic in influencing the way organizations perceive DT and whether any mitigating factors were considered when deciding to adopt new technology during the pandemic.

A qualitative approach was adopted in this research, consisting of 14 semi-structured interviews with eight senior managers and six employees, representing both the private and public sectors in New Zealand. All participants had in-depth knowledge of organizational DT adoption during the pandemic. Two separate sets of semi-structured interviews were used to enable comparison between senior managers' and employees' experiences of organizational adoption of DT post-emergence of COVID-19. Due to the nature of this research being conducted on organizational adoption of DT during the pandemic, time constraints and sample size were two of the key limitations of this research. Specifically, potential participants widely cited unavailability due to additional pressure from COVID-19. Given the limited research in this area, this study is explorative by nature and adds significant insights to the literature.

The findings suggest that COVID-19 has contributed towards an increased acceptance of, reliance on and adoption of DT across both organizational and social landscapes. The authors found that one of the reasons COVID-19 expedites the adoption of DT correlates with the notion of technology dependency, with organizations citing DT as a viable part of a business continuity plan (BCP) to counter the unpredictability of ongoing disruptive events associated with COVID-19 or any similar disruption which may be on the horizon. These findings are highly relevant as they suggest that the labor market in New Zealand is flexible so organizations and employees can adapt to DT and COVID-19.

This research adds much-needed insight into the emerging field of research that examines COVID-19's impact on the adoption of DT from both management and employee perspectives.

This paper aims to identify and prioritize the needs of coworking members. The authors focus on maintaining the existing members rather than attracting new ones.

The authors use two phases and multiple methods. The first phase focuses on a qualitative approach using observations and interviews to uncover and formulate the members’ needs. The second phase focuses on prioritizing the needs using a quantitative approach.

The authors discovered 19 member needs from the coworking spaces. Based on an online survey, the authors classified those needs into three main Kano model’s categories.

The resulting member needs and their strategic priorities provide a useful basis for coworking providers to direct their improvement efforts towards achieving greater member satisfaction.

Despite the relevance of how enterprise architecture (EA) contributes to organizational performance in contemporary digital technology-driven strategic renewal, little is known about the position of EA artifacts. Therefore, this study aims to build an integrative model of EA artifact-enabled EA value supplemented with a research agenda to enhance our understanding further.

This study leveraged grounded theory techniques and a systematic review approach to develop the integrative model and research agenda.

We inductively build a model of the position of EA artifacts in EA value creation. Additionally, we elaborate a research agenda that proposes (1) an investigation of the role of an EA practice in successful strategic change, (2) an examination of how to manage EA practice value generation and (3) longitudinal research to gain insight into the evolution of value creation by EA practices.

This study presents a model of EA artifact-enabled EA value, thereby contributing to our understanding of the mechanisms, inhibitors and success factors associated with EA value. Following our model, the proposed research agenda contains future research areas to help us better understand the mechanisms and interrelatedness of EA practices in highly dynamic environments.