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Prior research has extensively examined how bringing technology from work into the non-work life domain creates conflict, yet the reverse pathway has rarely been studied. The purpose of this study is to bridge this gap and examine how the non-work use of smartphones in the workplace affects work–life conflict.

Drawing from three literature streams: technostress, work–life conflict and role boundary theory, the authors theorise on how limiting employees' ability to integrate the personal life domain into work, by means of technology use policy, contributes to stress and work–life conflict. To test this model, the authors employ a natural experiment in a company that changed its policy from fully restricting to open smartphone access for non-work purposes in the workplace. The insights gained from the experiment were explored further through qualitative interviews.

Work–life conflict declines when a ban on using smartphones for non-work purposes in the workplace is revoked. This study's results show that the relationship between smartphone use in the workplace and work–life conflict is mediated by sensed stress. Additionally, a post-hoc analysis reveals that work performance was unchanged when the smartphone ban was revoked.

First, this study advances the authors' understanding of how smartphone use policies in the workplace spill over to affect non-work life. Second, this work contributes to the technostress literature by revealing how, in specific situations, engagement with ICT can reduce distress and strain.

This paper aims to empirically examine the impact of psychological factors (i.e. privacy and intrusiveness concerns) on user intentions regarding artificial intelligence (AI)-enabled social commerce applications at their core through perceived usefulness. The theoretical model is supported by the theory of planned behaviour (TPB).

Data was gathered from 488 social media users in Saudi Arabia.

Privacy concerns significantly affect perceived usefulness. Furthermore, the link between privacy concerns and behavioural intentions was mediated by perceived usefulness.

Business leaders should raise users’ awareness about the effectiveness of AI-powered tools that can influence their behavioural intentions. Furthermore, managers must be aware of the regulations that protect user privacy, track online activity and offer secure communication channels.

This paper expands on TPB by bridging the theoretical and practical divide. It further develops a theoretical framework for practitioners to better understand customers’ physiological aspects of using AI-powered social commerce platforms.

Este artículo examina empíricamente el impacto de los factores psicológicos (es decir, preocupaciones de privacidad e intrusión) en las intenciones de los usuarios con respecto a las aplicaciones de comercio social habilitadas con inteligencia artificial (IA) en su núcleo a través de la utilidad percibida. El modelo teórico se sustenta en la teoría del comportamiento planificado (TPB).

Los datos se recopilaron de 488 usuarios de redes sociales en Arabia Saudita.

Las preocupaciones sobre la privacidad afectan significativamente la utilidad percibida. Además, el vínculo entre las preocupaciones por la privacidad y las intenciones de comportamiento estuvo mediado por la utilidad percibida.

Los líderes empresariales deberían concienciar a los usuarios sobre la eficacia de las herramientas impulsadas por la IA que pueden influir en sus intenciones de comportamiento. Además, los gerentes deben conocer las regulaciones que protegen la privacidad de los usuarios, rastrear la actividad en línea y ofrecer canales de comunicación seguros.

Este artículo amplía el TPB cerrando la brecha teórica y práctica. Además, desarrolla un marco teórico para que los profesionales comprendan mejor los aspectos fisiológicos de los clientes al utilizar plataformas de comercio social impulsadas por IA.

本文透過實證研究了心理因素 (即隱私和侵入性問題) 對人工智慧 (AI) 驅動的社交商務應用程式的使用者意圖的影響, 其核心是透過感知有用性。 此理論模型得到計劃行為理論 (TBP) 的支持。

資料收集自沙烏地阿拉伯的 488 名社群媒體用戶。

隱私問題顯著影響感知的用處。 此外, 隱私問題和行為意圖之間的連結是透過感知有用性來調節的。

企業領導者應該提高使用者對人工智慧工具有效性的認識, 這些工具可以影響他們的行為意圖。 此外, 管理人員必須了解保護用戶隱私、追蹤線上活動並提供安全通訊管道的法規。

本文透過彌合理論和實踐鴻溝，對 TPB 進行了擴展。 它進一步為從業者開發了一個理論框架，以便更好地了解使用人工智慧驅動的社交商務平台的客戶的生理方面。

The purpose of this paper is to examine patient engagement in remote consultation services, an increasingly important issue facing Healthcare Operations Management (HOM) given the significant expansion in this and other forms of telehealth worldwide over the last decade. We use our analysis of the literature to develop a comprehensive framework that incorporates the patient journey, multidimensionality, antecedents and consequences, interventions and improvement options, as well as the cyclic nature of patient engagement. We also propose measures suitable for empirical assessment of different aspects of our framework.

We undertook a comprehensive review of the extant literature using a systematic review approach. We identified and analysed 63 articles published in peer-reviewed scientific journals between 2003 and 2022.

We conceptualise patient engagement with remote consultation across three key aspects: dimensions, process, and the antecedents and consequences of engagement. We identify nine contextual categories that influence such engagement. We propose several possible metrics for measuring patient engagement during three stages (before service, at/during service and after service) of remote consultation, as well as interventions and possible options for improving patient engagement therein.

The primary contribution of our research is the development of a comprehensive framework for patient engagement in remote consultation that draws on insights from literature in several disciplines. In addition, we have linked the three dimensions of engagement with the clinical process to create a structure for future engagement assessment. Furthermore, we have identified impact factors and outcomes of engagement in remote consultation by understanding which can help to improve levels of adoption, application and satisfaction, and reduce healthcare inequality. Finally, we have adopted a “cyclic” perspective and identified potential interventions that can be combined to further improve patient engagement in remote consultation.

The purpose of this study is to empirically examine the relationships between barrier-breakers and customers’ intention to fully adopt digital payment methods (DPMs).

Survey data were analyzed using statistical methods focusing on hypothesis testing with an ordinal regression model and moderation analysis using the PROCESS macro extension. Participants were divided into two groups of customers in Sweden: adopters-accepters, i.e. young bank customers and adopters-resisters, i.e. members of a formally organized group opposed to a cashless society.

The findings revealed that only the credibility barrier-breaker could increase the adopters-accepters’ intention to fully adopt DPMs. Credibility also seemed to be an important barrier-breaker for the adopters-resisters, as were perceived usefulness and social influence. Additional analyses showed that the impersonalization barrier reduces the impact of the barrier-breakers on DPM adoption.

Retail banks and merchants can use these results as a guide to what barrier-breakers might affect various customers’ intention to fully adopt DPMs, and to act accordingly. The impersonalization barrier also merits attention when creating an emotional connection to customers who use DPMs.

This study provides empirically based knowledge of the influence of barrier-breakers on the intention of customers, categorized as adopters-accepters and adopters-resisters, to fully adopt DPMs, and highlights the importance of maintaining a human touch in the post-COVID-19 digital era.

Consumer perception of corporate brand equity has primarily focused on product brand dimensions, neglecting considerations at the firm analysis level. Assessing corporate brands requires different criteria relevant to the competitiveness of companies, such as their prominence, management and meeting society’s demands. In this sense, this study aims to develop and validate a scale of corporate brand equity founded on consumer perceptions, transcending industry boundaries and comparing its relationship with companies' market share.

The authors used an integrative approach to clarify the construct’s domain, building on previous measures. They took several steps to select appropriate items, refine the measure, validate it through reliability tests and convergent and discriminant analyses, test the validity of the second-order formative structure of corporate brand equity and assess associations between first-order factors, the second-order factor and market share.

The model identifies three first-order dimensions of corporate brands (presence, outstanding management and responsible) that shape the second-order factor (corporate brand equity). They are directly related, but not proportionally, to market share, contributing to the general and joint assessment of the company’s competitive performance considering the consumer.

To the best of the authors’ knowledge, this study is the first attempt to develop a comprehensive measurement model of corporate brand equity that considers the firm level of analysis, combines metrics from previous research on corporate brand evaluation criteria and includes consumer perceptions of the company’s competitiveness, unifying branding theory with the theory of the marketing firm.

The purpose of this study is to provide current state of knowledge on how the COVID-19 emergency situation necessitated the behaviour influencing use and acceptance of telehealth. This study interlinks the health belief model (HBM) and the unified theory of acceptance and use of technology (UTAUT) to highlight the challenges and opportunities as a result of the COVID-19 pandemic in the public health sector.

This study used three online databases (Emerald publishing, Science Direct and Taylor and Francis) that enabled the authors to access electronic journal articles. Search strategy was used to extract articles based on the relevance of this study.

The key findings from this study suggested that the COVID-19 emergency forced health-care workers and their patients to rapidly use and rely on telehealth to reduce the rate of COVID-19 transmissions. The key benefits of telehealth use highlighted an expansive cost effective and convenient access to health-care services irrespective of geographical local and levels of physical impairment. Moreover, telehealth inhibited in person human interaction, which was perceived as impersonal and not ideal for new patient consultations. The barriers outweighed the benefits; as a result, it is unlikely that there will be a wide use of telehealth beyond the COVID-19 emergency situation.

The research findings are limited to discussions drawn from available secondary data. The criteria within telehealth for policymakers to note the technology acceptance and use for both health-care and outpatient stakeholders and their health seeking behaviour. Health-care sectors (private and public) and government need to understand enablers of effective telehealth in policymaking to ease the barriers during an emergency situation like a pandemic.

This study contributes to the emerging literature on how COVID-19 pandemic has disrupted and accelerated telehealth by extending both the UTAUT and HBM theories. This study is expected to contribute and expand literature on telehealth during emergency situations, given the novice nature of COVID-19 and limited literature surrounding it.

This study was conducted to address the methodical shortcomings and high associated cost of understanding the use of new, poorly understood architectural spaces, such as makerspaces. The proposed quantified method of enhancing current post-occupancy evaluation (POE) practices aims to provide architects, engineers and building professionals with accessible and intuitive data that can be used to conduct comparative studies of spatial changes, understand changes over time (such as those resulting from COVID-19) and verify design intentions after construction through a quantified post-occupancy evaluation.

In this study, we demonstrate the use of ultra-wideband (UWB) technology to gather, analyze and visualize quantified data showing interactions between people, spaces and objects. The experiment was conducted in a makerspace over a four-day hackathon event with a team of four actively tracked participants.

The study shows that by moving beyond simply counting people in a space, a more nuanced pattern of interactions can be discovered, documented and analyzed. The ability to automatically visualize findings intuitively in 3D aids architects and visual thinkers to easily grasp the essence of interactions with minimal effort.

By providing a method for better understanding the spatial and temporal interactions between people, objects and spaces, our approach provides valuable feedback in POE. Specifically, our approach aids practitioners in comparing spaces, verifying design intent and speeding up knowledge building when developing new architectural spaces, such as makerspaces.

Plant cultivation holds a pivotal role in agriculture, necessitating precise disease identification for the overall health of plants. This research conducts a comprehensive comparative analysis between two prominent deep learning algorithms, convolutional neural network (CNN) and DenseNet121, with the goal of enhancing disease identification in tomato plant leaves.

The dataset employed in this investigation is a fusion of primary data and publicly available data, covering 13 distinct disease labels and a total of 18,815 images for model training. The data pre-processing workflow prioritized activities such as normalizing pixel dimensions, implementing data augmentation and achieving dataset balance, which were subsequently followed by the modeling and testing phases.

Experimental findings elucidated the superior performance of the DenseNet121 model over the CNN model in disease classification on tomato leaves. The DenseNet121 model attained a training accuracy of 98.27%, a validation accuracy of 87.47% and average recall, precision and F1-score metrics of 87, 88 and 87%, respectively. The ultimate aim was to implement the optimal classifier for a mobile application, namely Tanamin.id, and, therefore, DenseNet121 was the preferred choice.

The integration of private and public data significantly contributes to determining the optimal method. The CNN method achieves a training accuracy of 90.41% and a validation accuracy of 83.33%, whereas the DenseNet121 method excels with a training accuracy of 98.27% and a validation accuracy of 87.47%. The DenseNet121 architecture, comprising 121 layers, a global average pooling (GAP) layer and a dropout layer, showcases its effectiveness. Leveraging categorical_crossentropy as the loss function and utilizing the stochastic gradien descent (SGD) Optimizer with a learning rate of 0.001 guides the course of the training process. The experimental results unequivocally demonstrate the superior performance of DenseNet121 over CNN.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.