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This study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating parameters in microfluidic platform, attaining precise size and frequency of droplet generation.

The photolithography method is utilized to prepare the microfluidic devices used in this study, and various experiments are conducted at various flow-rate and viscosity ratios. Data for droplet shape is collected to train the artificial intelligence (AI) models.

Growth phase of droplets demonstrated a unique spring back effect in droplet size. The fully developed droplet sizes in the microchannel were modeled using least absolute shrinkage and selection operators (LASSO) regression model, Gaussian support vector machine (SVM), long short term memory (LSTM) and deep neural network models. Mean absolute percentage error (MAPE) of 0.05 and R² = 0.93 were obtained with a deep neural network model on untrained flow data. The shape parameters of the droplets are affected by several uncontrolled parameters. These parameters are instinctively captured in the model.

Experimental data set is generated for varying viscosity values and flow rates. The variation of flow rate of continuous phase is observed here instead of dispersed phase. An automated computation routine is developed to read the droplet shape parameters considering the transient growth phase of droplets. The droplet size data is used to build and compare various AI models for predicting droplet sizes. A predictive model is developed, which is ready for automated closed loop control of the droplet generation.

The article aims to study the demand for sparkling wines in Europe. The main objective is to estimate the classic demand parameters aggregately for the entire European area (European Union and the United Kingdom) and separately for groups of countries characterized by wine markets with similar characteristics.

Using 15-years market data for different wine categories from the Euromonitor Passport database, the estimation of price and income elasticities is performed through a Quadratic Almost Ideal Demand System. In line with the objectives, the model is applied first to the whole European area and then separately to the considered groups of countries (subareas). To identify homogeneous subareas, a cluster analysis was performed on basic characteristics of the wine market.

When considering the European market as a whole, sparkling wines should be considered a luxury category with a high own-price elasticity. However, the structure of their demand is rather different in different sub-areas. The observed heterogeneity suggests that differentiated policy and marketing considerations should be made. In addition, it widens the possibilities for producers, who can choose the submarkets that respond best to their needs to export their sparkling wines. This seems particularly important in markets, like the sparkling wine ones, that are experiencing a continuous expansion over the last decades.

Despite using a methodology well-established to study wine and alcohol demand, the study fills a considerable gap in the literature. Although the demand for sparkling wine is growing worldwide, so far only a couple of studies have engaged in the analysis of its structure. In Europe, the largest market for sparkling wine, this kind of studies is completely lacking.

Women migrant street vendors are considered a vulnerable group in societies, thereby being hard hit during a crisis. This research aims to examine effects of COVID-19 social distancing on their businesses, consumption, health and general lives; solutions and mitigation strategies that they adopted in response to these adverse effects; and their recovery of socioeconomic lives after social distancing.

The data were collected from the women migrant street vendors in urban Vietnam. Descriptive statistics, probit model and ordered probit model were used for empirical analysis.

Women migrant street vendors faced immense challenges during social distancing. They also lacked solutions to sustain their businesses and were forced to resort to various mitigation strategies. Their socioeconomic lives were also slow to recover after social distancing. In addition, those experiencing greater business loss faced more adverse effects during social distancing and have more difficulties in the recovery of socioeconomic lives after social distancing.

This research highlights the importance of redesigning social policies to support women migrant street vendors during crises. It also emphasizes the need to formalize and legalize their activities to foster sustainable and inclusive development in the long term.

To the best of the authors’ knowledge, this research is among the first attempts to explore the effects of COVID-19 social distancing on the women migrant street vendors and to examine how they respond to these adverse effects.

Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

The main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).

The study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.

Top management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.

This study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.

Artificial Intelligence (AI) is an emerging technology and turned into a field of knowledge that has been consistently displacing technologies for a change in human life. It is applied in all spheres of life as reflected in the review of the literature section here. As applicable in the field of libraries too, this study scientifically mapped the papers on AAIL and analyze its growth, collaboration network, trending topics, or research hot spots to highlight the challenges and opportunities in adopting AI-based advancements in library systems and processes.

The study was developed with a bibliometric approach, considering a decade, 2012 to 2021 for data extraction from a premier database, Scopus. The steps followed are (1) identification, selection of keywords, and forming the search strategy with the approval of a panel of computer scientists and librarians and (2) design and development of a perfect algorithm to verify these selected keywords in title-abstract-keywords of Scopus (3) Performing data processing in some state-of-the-art bibliometric visualization tools, Biblioshiny R and VOSviewer (4) discussing the findings for practical implications of the study and limitations.

As evident from several papers, not much research has been conducted on AI applications in libraries in comparison to topics like AI applications in cancer, health, medicine, education, and agriculture. As per the Price law, the growth pattern is exponential. The total number of papers relevant to the subject is 1462 (single and multi-authored) contributed by 5400 authors with 0.271 documents per author and around 4 authors per document. Papers occurred mostly in open-access journals. The productive journal is the Journal of Chemical Information and Modelling (NP = 63) while the highly consistent and impactful is the Journal of Machine Learning Research (z-index=63.58 and CPP = 56.17). In the case of authors, J Chen (z-index=28.86 and CPP = 43.75) is the most consistent and impactful author. At the country level, the USA has recorded the highest number of papers positioned at the center of the co-authorship network but at the institutional level, China takes the 1st position. The trending topics of research are machine learning, large dataset, deep learning, high-level languages, etc. The present information system has a high potential to improve if integrated with AI technologies.

The number of scientific papers has increased over time. The evolution of themes like machine learning implicates AI as a broad field of knowledge that converges with other disciplines. The themes like large datasets imply that AI may be applied to analyze and interpret these data and support decision-making in public sector enterprises. Theme named high-level language emerged as a research hotspot which indicated that extensive research has been going on in this area to improve computer systems for facilitating the processing of data with high momentum. These implications are of high strategic worth for policymakers, library stakeholders, researchers and the government as a whole for decision-making.

The analysis of collaboration, prolific authors/journals using consistency factor and CPP, testing the relationship between consistency (z-index) and impact (h-index), using state-of-the-art network visualization and cluster analysis techniques make this study novel and differentiates it from the traditional bibliometric analysis. To the best of the author's knowledge, this work is the first attempt to comprehend the research streams and provide a holistic view of research on the application of AI in libraries. The insights obtained from this analysis are instrumental for both academics and practitioners.

Research done to date has produced a wide range of perspectives that center on the junction between the specific function and responsibility of the entrepreneur when applying ethical dimensions to the field of entrepreneurship. By combining a bibliometric analysis with the literature review, the purpose of this paper is to provide an overview of entrepreneurship ethics and its contribution to sustainable development goals, along with future research directions on the topic of entrepreneurship and ethics.

The research looked for literature based on entrepreneurship ethics from the Dimension database. The authors accumulated 2,279 articles for the period of 2002–2022 for analysis. The authors used bibliometric analysis for analyzing the topic with the use of VosViewer and Excel.

By combining a bibliometric analysis with a literature review, this study aimed to provide an overview of the ethics and entrepreneurship and contribution to sustainable development goals. The most influential author and journal are Vanessa Ratten and the Journal of Business Ethics, respectively. The top sustainable development goals (SDGs) being contributed by entrepreneurship ethics are SDG4, SDG8, SDG16, SDG11 and SDG3. By contrasting these two interpretations, the authors have demonstrated that the entrepreneurship environment involves various ethical issues connected to personal, organizational and societal sectors.

The current study may serve as a general guideline for future investigation into this topic.

Regarding the practical ramifications, the findings can help entrepreneurs who are preparing to launch a business or are already doing so. The findings provide a comprehensive framework of success variables that must be considered to improve the venture’s performance.

This study’s originality is from presenting a structured and in-depth literature review that describes the current state of entrepreneurship ethics and its contribution to sustainable development goals. A complete analysis of the data gathered on entrepreneurship ethics is offered to establish that it is sustainable.

Drawing on signaling and social identity theories, the main objective of this paper is to suggest and validate a research framework to investigate the association of corporate social responsibility (CSR) perceptions with organizational attractiveness by uncovering the underlying mechanism of organizational trust.

By conducting regression analysis, the study hypotheses were tested with the data collected from a snowball convenient sample of 318 potential job seekers.

The study results revealed that prospective applicants' perceptions about a company's CSR undertakings are significantly associated with their trust in and attraction to the company. Noticeably, organizational trust partially mediated the direct relationship between CSR perceptions and organizational attractiveness.

Based on the study results, some specific theoretical advancements and helpful insights have been highlighted for human resources (HR) practitioners in today's competing organizations. The paper ends by outlining key research drawbacks and directions for possible work in the future.

Despite the plausible underlying role of organizational trust in the link between CSR and organizational attraction, empirical studies of this theoretical phenomenon are still lacking in the context of pre-hire recruitment literature. This may be the first study that identifies organizational trust as a salient psychological factor through which job seekers' CSR perceptions become more instrumental in influencing their attraction to a firm.

Adoption of Clinical Decision Support Systems (CDSS) is a crucial step towards the digital transition of the healthcare sector. This review aims to determine and synthesise the influential factors in CDSS adoption in inpatient healthcare settings in order to grasp an understanding of the phenomenon and identify future research gaps.

A systematic literature search of five databases (Medline, EMBASE, PsycINFO, Web of Science and Scopus) was conducted between January 2010 and June 2023. The search strategy was a combination of the following keywords and their synonyms: clinical decision support, hospital or secondary care and influential factors. The quality of studies was evaluated against a 40-point rating scale.

Thirteen papers were systematically reviewed and synthesised and deductively classified into three main constructs of the Technology–Organisation–Environment theory. Scarcity of papers investigating CDSS adoption and its challenges, especially in developing countries, was evident.

This study offers a summative account of challenges in the CDSS procurement process. Strategies to help adopters proactively address the challenges are: (1) Hospital leaders need a clear digital strategy aligned with stakeholders' consensus; (2) Developing modular IT solutions and conducting situational analysis to achieve IT goals; and (3) Government policies, accreditation standards and procurement guidelines play a crucial role in navigating the complex CDSS market.

To the best of the authors’ knowledge, this is the first review to address the adoption and procurement of CDSS. Previous literature only addressed challenges and facilitators within the implementation and post-implementation stages. This study focuses on the firm-level adoption phase of CDSS technology with a theory refining lens.

This paper aims to explore factors influencing suppliers' acceptance, integration challenges, expected benefits and support from customers when implementing a customer-introduced digital supply chain system.

The study investigates the perspective of suppliers using a mixed methodology approach that combines qualitative interviews with a large-scale quantitative survey conducted among 220 internationally located suppliers of an automotive-industrial firm.

As a result, the authors identified 11 factors that drive suppliers' acceptance of customer-introduced digital supply chain systems. These factors have been ranked based on their importance. The top three important factors identified were the digital system being provided at no cost to the suppliers, the system's ability to save time and the system offering benefits to the suppliers.

Further research can be conducted to validate the perspective of suppliers in other industries. Additionally, future studies can investigate the effectiveness of fulfilling these acceptance factors within an actual digital integration setup.

Companies can leverage these insights to accelerate their digital supply chain integration efforts. The insights on acceptance factors, challenges, benefits and support expected by suppliers can serve as a valuable guide for policy and decision makers within the industry.

To the best of the authors’ knowledge, this study is among the first to investigate the perspective of suppliers in the integration of a customer's digital supply chain. By including the supplier's perspective, this study makes a significant contribution to the academic literature about supply chain digitalisation.