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A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

An important but neglected area of investigation in digital entrepreneurship is the combined role of both core and peripheral members of an emerging technological field in shaping the symbolic and social boundaries of the field. This is a serious gap as both categories of members play a distinct role in expanding the pool of resources of the field. I address this gap by exploring how membership category is related to funding decisions in the emerging field of artificial intelligence (AI).

The first quantitative study involved a sample of 1,315 AI-based startups which were founded in the period of 2011–2018 in the United States. In the second qualitative study, the author interviewed 32 members of the field (core members, peripheral members and investors) to define the boundaries of their respective role in shaping the social boundaries of the AI field.

The author finds that core members in the newly founded field of AI were more successful at attracting funding from investors than peripheral members and that size of the founding team, number of lead investors, number of patents and CEO approval were positively related to funding. In the second qualitative study, the author interviewed 30 members of the field (core members, peripheral members and investors) to define their respective role in shaping the social boundaries of the AI field.

This study is one of the first to build on the growing literature in emerging organizational fields to bring empirical evidence that investors adapt their funding strategy to membership categories (core and peripheral members) of a new technological field in their resource allocation decisions. Furthermore, I find that core and peripheral members claim distinct roles in their participation and contribution to the field in terms of technological developments, and that although core members attract more resources than peripheral members, both actors play a significant role in expanding the field’s social boundaries.

Core AI entrepreneurs who wish to attract funding may consider operating in fewer categories in order to be perceived as core members of the field, and thus focus their activities and limited resources to build internal AI capabilities. Entrepreneurs may invest early in filing a patent to signal their in-house AI capabilities to investors.

The social boundaries of an emerging technological field are shaped by a multitude of actors and not only the core members of the field. The author should pay attention to the role of each category of actors and build on their contributions to expand a promising field.

This paper is among the first to build on the growing literature in emerging organizational fields to study the resource acquisition strategies of entrepreneurs in a newly establishing technological field.

The aim of this study is to comprehend the behavioural intention of females' perception toward smart healthcare technology. The study also examines the moderation effect of social influences between perceived smart healthcare technology and perceived usefulness among female users.

To test the model, this study collected data from female respondents (n = 913) responses. The data were analyzed by structural equation modeling (SEM) using Smart-PLS 3.2. To complement the findings from structural equation modeling, the study also conducted a post-hoc test via experimental research design. The authors also applied a t-test and PROCESS macro analysis to re-confirm the relationship mentioned above.

The findings revealed that perceived ease of use significantly mediates the relationship between females' perceived smart healthcare technology and intention to use. The findings also show that social influence moderates between smart healthcare technology and the perceived usefulness relationship.

Social influence is one of the major issues while adopting smart healthcare technology because the respondents perceived that they are accustomed to the technologies related to smart health once their surroundings and social environment influence them.

The current study is a pioneer in the context of a developing country and unique in that it makes two contributions: it extends previous research on smart health technology adoption in the healthcare business by considering females, and it gives a broad knowledge of the female healthcare consumers from emerging nations which can be useful for developing technology-driven healthcare services strategies.

Healthcare supply chains (HCSCs) face severe challenges when compared to regular chains. Besides avoiding bankruptcy, they must accomplish their goal which is to save lives. Since 2019 the COVID-19 pandemic evidenced that a HCSC disruption generates disruptions to other SCs. Therefore, the objective of this paper is threefold: conduct a systematic literature review to build a HCSC operational excellence (HSCOE) definition; build a conceptual framework by mapping the antecedents of HSCOE and formulate hypotheses; test the hypotheses using a fuzzy-Set Qualitative Comparative Analysis (fsQCA) combined with partial least squares structural equation modeling (PLS-SEM) techniques to obtain empirical validation.

Given this context, this paper conducted a systematic literature review to build a HSCOE conceptual framework and used a fsQCA combined with PLS-SEM techniques to obtain empirical validation.

The paper revealed a relationship between important variables to achieve HSCOE, such as Supply chain 4.0, SC risk management, SC integration, SC resilience (antecedents) and HSCOE (consequent).

The literature contributions of this paper are as follows: validating a new scale for each of the constructs; finding evidence of the causal relationships between the latent variables; measuring how the constructs influence the HSCOE; in addition, the results address important literature gaps identified by researchers and serve as a guide to organizations that need to implement these practices. Furthermore, this study recommends that HCSC managers consider the implementation of robust initiatives concerning the latent variables presented in this work.

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

This study aims to examine the mediating role of social influence on the relationship between key predictors of E-pharmacy adoption among young consumers based on the unified theory of adoption and use of technology (UTAUT).

This study employs a quantitative correlational research design. Based on cluster sampling, data was collected from 306 university students from three public universities in southwestern Nigeria. Data was analysed using partial least square structural equation modeling.

The primary determinant driving the adoption of e-pharmacy is performance expectancy. Social influence plays a partial mediating role in linking performance expectancy to e-pharmacy adoption. In contrast, it fully mediates the relationship between effort expectancy, facilitating conditions and the adoption of e-pharmacy services.

This study provides theoretical clarity on recent issues within the UTAUT framework. Findings highlight the complexity of how social factors interact with individual beliefs and external conditions in determining technology acceptance.

Research includes information relevant to access the impact of e-pharmacy services on healthcare accessibility, affordability and quality in developing countries.

The findings extend the adoption of technology literature in healthcare and offer a new understanding of adoption dynamics. The results emphasize the importance of performance expectancy in driving e-pharmacy adoption, providing a clear direction for stakeholders to enhance service quality and user experience of e-pharmacy. Additionally, the mediating effect of social influence highlights the significance of peer recommendations, celebrity endorsements and social media campaigns in shaping consumer adoption of e-pharmacies among young people.

The mental healthcare is experiencing an ever-growing surge in understanding the consumer (e.g., patient) engagement paradox, aiming to vouch for the quality of care. Despite this surge, scant attention has been given in academia to conceptualize and empirically investigate this particular aspect. Thus, drawing on the Stimulus-Organism-Response (S-O-R) paradigm, the study explores how patients engage with healthcare service providers and how they perceive the quality of the healthcare services.

Data were collected from 279 respondents, and the derived conceptual model was tested by using Smart PLS 3.2.7 and PROCESS. To complement the findings of partial least squares (PLS)-based structural equation modeling (SEM), the present study also applied fuzzy set qualitative comparative analysis (fsQCA) to identify the necessary and sufficient conditions to explore substitute conjunctive paths that emerge.

Findings show that patients’ perceived intimacy (PI), cohesion and privacy enhance the quality of mental healthcare service providers. The results also suggest that patients’ PI, cohesion and privacy have indirect effects on the perceived quality of care (PQC) by the service providers through consumer engagement. The fsQCA results derive that the relationship among conditions leading to patients’ perception of the quality of care in regard to mental healthcare service providers is complex and is best reflected as multiple and conjectural causation configurations.

The findings from this research contribute to the advancement of studies on patients’ experiences by empirically examining the unique dynamics of interaction between consumers (patients) and mental healthcare service providers, thereby enriching both the literature on social interactions and the understanding of the consumer–provider relationship.

The results of this study provide practical implications for mental healthcare service providers on how to combine the study variables to enhance the quality of care and satisfy more patients.

A significant research gap has ascertained the inter-relationship between PI, cohesion, privacy, engagement and PQC from the perspective of mental healthcare service providers. This research is one of the primary studies from a managerial and methodological standpoint. The study contributes by combining symmetric and asymmetric statistical tools in service marketing and healthcare research. Furthermore, the application of fsQCA helps to understand the interactions that might not be immediately obvious through traditional symmetric methods.

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 objective of this paper is to examine the profound repercussions of workplace bullying (WB), emotional exhaustion (EE), and psychological distress (PD) on poor job performance (PJP) within the intricacies of Thailand’s healthcare sector. It also seeks to elucidate the moderating influence of COVID-19 burnout (CBO) on these variables.

This paper utilized a quantitative research approach. A total of 230 responses were collected from healthcare workers using convenience sampling during a significant surge of the coronavirus in March 2022. To assess the reliability and correlations between constructs, a dual-stage structural equation modeling (SEM) technique was applied.

During the global health crisis caused by COVID-19, WB and PD were found to positively predict PJP, except for EE. The presence of WB elevated EE and PD among Thai hospital staff. PD and EE partially mediated the relationship between WB and PJP. The positive moderating role of CBO among hospital employees significantly buffered the relationship between WB and EE.

The originality of this study lies in the examination of the poor mental health of Thai healthcare workers during the COVID-19 pandemic. Healthcare reforms are required to protect the mental health of Thai healthcare staff to prevent poor job performance following unprecedented circumstances.