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Improving productivity and efficiency has always been crucial for industrial companies to remain competitive. In recent years, the topic of environmental impact has become increasingly important. Published research indicates that environmental and economic goals can enforce or rival each other. However, few papers have been published that address the interaction and integration of these two goals.

In this paper, we identify both, synergies and trade-offs based on a systematic review incorporating 66 publications issued between 1992 and 2021. We analyze, quantify and cluster examples of conjunctions of ecological and economic measures and thereby develop a framework for the combined improvement of performance and environmental compatibility.

Our findings indicate an increased significance of a combined consideration of these two dimensions of sustainability. We found that cases where enforcing synergies between economic and ecological effects were identified are by far more frequent than reports on trade-offs. For the individual categories, cost savings are uniformly considered as the most important economic aspect while, energy savings appear to be marginally more relevant than waste reduction in terms of environmental aspects.

No previous literature review provides a comparable graphical treatment of synergies and trade-offs between cost savings and ecological effects. For the first time, identified measures were classified in a 3 × 3 table considering type and principle.

The purpose of this research is as follows: DPP-BOH-PVA has been synthesized from 1,1′:3′,1″-terphenyl-5'-boronic acid (DPP-OH) and polyvinyl alcohol (PVA). The afterglow lifetime of DPP-BOH-PVA was studied by changing contents of DPP-OH (1, 2 and 4 Wt.%). These films were characterized with Fourier transform infrared, X-ray diffraction as structural analysis and DSC as thermal analysis. Afterglow lifetimes were evaluated as time-resolved emission decay profile analysis. Fiber films of DPP-BOH-PVA-2-E have been prepared by electrospinning method with the diameter of 5 μm and afterglow life time of 2.1 s (@ 535 nm) under ambient conditions. Stimulus responsive properties with afterglow emission for fiber film were investigated.

During the synthesis of the polymer, modification was carried out using DPP-OH/PVA with a molar ratio of 1/4, under an alkalinity medium with ammonium hydroxide and with a temperature of 80°C.

XRD results indicate that DPP-BOH-PVA film had high crystallinity, which is crucial for preparing organic room temperature phosphorescence (RTP) materials.

The reaction mixture must be stirred continuously. Temperature should be controlled to prevent the rapid evaporation of ammonium hydroxide.

This study provides technical information for the synthesis of multidimensional stimulation response RTP micron fiber thin film. The electrospinning technology may also promote the applications of the large areas of RTP films.

This resin will be used for the multidimensional stimulation response RTP fiber thin film.

The diameter of fiber film of PP-BOH-PVA-2-E by electrospinning method was in the range of 5 μm, and its afterglow lifetime decayed to be 2.1 s.

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.

This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces.

The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor.

All 46 factors were assessed as “Very Important” through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are “AI ability to learn from patient data”, “insufficient data privacy measures for patients”, “availability of technical support and maintenance services”, “physicians’ acceptance of AI in healthcare”, “reliability and uptime of AI systems” and “ability to reduce medical errors”.

Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices.

This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders’ comprehension of the factors challenging the integration of AI technologies.

This study explores whether and how four main factors of short-branded video content (content matching, information relevance, storytelling and emotionality) facilitate consumer engagement (likes, comments and shares), as well as the moderating effect of the release time (morning, afternoon and evening) in such relationships.

This study uses Python to write programs to crawl relevant data information, such as consumer engagement and short video release time. It combines coding methods to empirically analyze the impact of short-branded video content characteristics on consumer engagement. A total of 10,240 Weibo short videos (total duration: 238.645 h) from 122 well-known brands are utilized as research objects.

Empirical results show that the content characteristics of short videos significantly affected consumer engagement. Furthermore, the release time of videos significantly moderated the relationship between the emotionality of short videos and consumer engagement. Content released in the morning enhanced the positive impact of warmth, excitement and joy on consumer engagement, compared to that released in the afternoon.

The findings provide new insights for the dissemination of products and brand culture through short videos. The authors suggest that enterprises that use brand videos consider content matching, information relevance, storytelling and emotionality in their design.

From a broader perspective, this study constructs a new method for comprehensively evaluating short-branded video content, based on four dimensions (content matching, information relevance, storytelling and emotionality) and explores the value of these dimensions for creating social media marketing success, such as via consumer engagement.

The growing focus on socially responsible supply chain management (SRSCM) has made it crucial to extend corporate social responsibility (CSR) to upstream suppliers. Drawing on resource dependence theory, this study aims to examine how supplier dependence upon socially responsible buyers impacts suppliers' CSR performance and how this relationship is moderated by network prominence and demand uncertainty.

The proposed hypotheses are tested using regression analysis with Heckman's two-stage model and a dyadic supply chain dataset constructed based on publicly traded Chinese firms between 2008 and 2016. This time window is selected due to a one-year lag of the dependent variable and the change in evaluation methods of the database providing CSR performance in 2018.

The empirical results indicate that supplier dependence upon socially responsible buyers is positively associated with suppliers' CSR performance. However, this positive relationship is attenuated when suppliers occupy a prominent position in the network or when they face high demand uncertainty.

This study extends knowledge about the role of relationship dependence in implementing SRSCM by highlighting its positive impact on suppliers' CSR. Thus, this study contributes to the buyer–supplier relationship literature and the power and relationship dependence literature. This study further advances the understanding of the factors that influence suppliers' behavior by exploring the moderating roles of network prominence and demand uncertainty. The results have several practical implications for managers and policymakers.

The current research elucidates the role of empathy in design of artificial intelligence (AI) systems in healthcare context, through a structured literature review, analysis and synthesis of academic literature published between 1990 and 2024.

This study aims to advance the domain of empathy in AI by adopting theory constructs context method approach using the PRISMA 2020 framework.

The study presents a current state-of-the-art literature to review the connections between empathy and AI and identifying four clusters showing the emerging trajectories in the field of AI and empathy in healthcare setting.

Despite a rise in empirical research, the potential pathways enhancing AI accountability by incorporation of empathy is unclear. The research aims to contribute to the existing literature on AI and empathy in the healthcare sector by carving out four distinct clusters depicting the future research avenues.

This study aims to identify the librarians’ readiness to leverage artificial intelligence for sustainable competence development and smart library services.

This study used a quantitative research design for addressing the objectives. The population consisted of librarians from the public and private sector universities of Pakistan. The data were analyzed by using Smart PLS software.

The analysis consisted of two major parts: first the assessment of measurement model and second the structural equation modeling analysis. A significant positive impact of AI adoption was found on the implementation smart library services. Findings revealed that behavioral intention motivated librarians to adopt AI tools in university libraries for the delivery of smart library services.

We applied quantitative method to carry out the study while future authors may conduct a systematic literature review on the same topic for offering a broader outlook.

It has provided practical contributions by providing a baseline for management bodies to construct policies for the successful adoption of AI in libraries for sustainable competence development of practicing librarians and implementation of smart library services.

The study has social implications too as AI integrated library services prove fruitful for society and digitally skilled librarians play a vital role for the promotion of reading and research culture in society.

To the best of the authors’ knowledge, this is the first study on librarians’ readiness to leverage artificial intelligence for the enhancement of digital literacy skills, sustainable competence development and smart library services in the context of Pakistan.

This paper explores how healthcare administration students perceive the integration of Artificial Intelligence (AI) in healthcare leadership, mainly focusing on the sustainability aspects involved. It aims to identify gaps in current educational curricula and suggests enhancements to better prepare future healthcare professionals for the evolving demands of AI-driven healthcare environments.

This study utilized a cross-sectional survey design to understand healthcare administration students' perceptions regarding integrating AI in healthcare leadership. An online questionnaire, developed from an extensive literature review covering fundamental AI knowledge and its role in sustainable leadership, was distributed to students majoring and minoring in healthcare administration. This methodological approach garnered participation from 62 students, providing insights and perspectives crucial for the study’s objectives.

The research revealed that while a significant majority of healthcare administration students (70%) recognize the potential of AI in fostering sustainable leadership in healthcare, only 30% feel adequately prepared to work in AI-integrated environments. Additionally, students were interested in learning more about AI applications in healthcare and the role of AI in sustainable leadership, underscoring the need for comprehensive AI-focused education in their curriculum.

The research is limited by its focus on a single academic institution, which may not fully represent the diversity of perspectives in healthcare administration.

This study highlights the need for healthcare administration curricula to incorporate AI education, aligning theoretical knowledge with practical applications, to effectively prepare future professionals for the evolving demands of AI-integrated healthcare environments.

This research paper presents insights into healthcare administration students' readiness and perspectives toward AI integration in healthcare leadership, filling a critical gap in understanding the educational needs in the evolving landscape of AI-driven healthcare.

This study explores the integration of Artificial Intelligence (AI) within family businesses. It seeks to understand how family-owned enterprises navigate the adoption of AI technologies amidst balancing traditional business values and the imperatives of digital transformation. The research addresses the gap in the existing literature by providing insights into the strategic, operational and cultural dynamics influencing AI adoption in family businesses, highlighting the unique challenges and opportunities they face in leveraging AI for competitive advantage while preserving their legacy.

Employing a qualitative research design, this study utilizes semi-structured interviews with key stakeholders in Turkish family businesses actively engaging in AI projects. Purposive sampling was adopted to ensure a diverse representation of industries and AI adoption stages. The interviews aimed to capture in-depth insights into the motivations, strategies and outcomes of AI integration within these enterprises. Thematic analysis was conducted on the interview transcripts to identify recurring themes and patterns, providing a nuanced understanding of the factors driving AI adoption decisions in the context of family business values and traditions.

The findings reveal that family businesses in Turkey perceive AI as a strategic tool to enhance operational efficiency and customer engagement. However, integrating AI technologies is often met with challenges, including resource constraints, digital literacy gaps and concerns over maintaining family legacy. Notably, businesses that successfully navigate AI adoption tend to employ tailored strategies that align with their core values, involving key family members in the decision-making process and fostering a culture of innovation. The study also highlights the importance of ethical considerations and governance in ensuring AI initiatives resonate with the family business ethos.

The study’s reliance on qualitative interviews within a single country context limits the generalizability of the findings. Future research could expand the geographical scope and incorporate quantitative methods to validate the identified themes across broader populations. Additionally, exploring the impact of generational differences within family businesses on AI adoption could offer more profound insights. The study underscores the need for a more nuanced understanding of the interplay between technology and tradition in family businesses, suggesting avenues for further investigation into how these enterprises can leverage AI to foster innovation while preserving their legacy.

This research offers practical guidance for family businesses contemplating AI integration. It emphasizes the importance of aligning AI strategies with family values and involving stakeholders across generations in the adoption process. The findings suggest that family businesses can benefit from investing in digital literacy and fostering a culture open to technological innovation. Additionally, the study highlights the need for robust governance structures to navigate ethical considerations in AI adoption. By adopting a strategic approach to AI integration, family businesses can enhance their competitiveness without compromising their core values, ensuring long-term sustainability and success in the digital era.

Integrating AI in family businesses has significant social implications, particularly regarding employment and preserving the family legacy. The study suggests that thoughtful AI adoption can contribute to job creation and skill development, counteracting concerns over job displacement. Moreover, by leveraging AI to align with their core values, family businesses can reinforce their role as stewards of social and economic stability within their communities. This research underscores the potential of AI to support the intergenerational transfer of knowledge and values, fostering innovation while preserving the unique cultural heritage of family enterprises.

This study contributes to the emerging literature on AI adoption in family businesses by exploring the Turkish context. It fills a gap in the literature by examining the unique challenges and opportunities family businesses face in integrating AI, highlighting the interplay between technological innovation and traditional values. The research offers valuable insights into tailored strategies for successful AI adoption that respect the legacy and ethos of family enterprises. By focusing on the socio-cultural dimensions of technology integration, this study enriches our understanding of how family businesses can navigate digital transformation while preserving their identity.