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The promulgation of group purchasing organizations (GPOs) into the healthcare (HC) sector is an invaluable procurement strategy to manage the suppliers effectively. This study aims to identify and prioritize the factors of integrating GPOs into the HC sector on the perspectives of the developing countries such as India.

The factors are identified from current literature exploration, experts’ support and experience surveys. The factors are scrutinized and shortlisted using the Delphi technique and analysed further using the best-worst model method.

The findings of the study highlight the cost reduction, fair distribution of savings and healthcare supply chain (HCSC) data standardization among others to be the most prioritized drivers. The consulting services provided by GPOs including training and development as a result of high competitiveness in the HC market has been prioritized the least.

The study bears some important implications for decision and policymakers. The managers should consider factors, namely, cost reduction, fair distribution of savings and HCSC data standardization on a priority basis that acts as motivation for the HC providers to join the GPOs.

The study provides valuable insights for HC providers to participate in the GPOs for cost savings and enhance the performances.

The study aims to envisage upon conceptualizing and developing the scales of smart health-care supply chain (HCSC) performance in the era of the fourth industrial revolution.

This study has implemented structural equation modelling to analyse the survey data. To analyse the collected data from the field investigation involving a sample size of 323, the IBM SPSS AMOS 26 software package is considered to implement exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in this study.

The measurement model of the study developed using EFA and CFA has resulted in validating 32 items out of the 42 items. Resultantly, the analysis using the above-mentioned tools and the parsimony of items to scale development makes it more susceptible to contributing significantly to the current HCSC literature.

The HC providers need to consider a holistic and systematic approach while taking into account the constructs of smart HCSC performance, specifically, the effect of HCSC responsiveness and industry 4.0 between the independent and dependent variables. The scales are validated from the perspectives of developing countries such as India, and hence, their generalizability with respect to first-world countries is practically limited.

The scales validated in this study would facilitate managers and key decision-makers to apply the various elements of HCSC practices, gauge the application of these scales and monitor the performance of health-care facilities.

Although wearable health-monitoring technology (WHMT) has become a stimulus for public health, women’s acceptance rate of this technology appears to be low. Thus, this study intends to investigate the factors affecting women’s adoption of WHMT.

The unified theory of acceptance and use of technology–2 model has been used in this study as a research framework that has been extended to include lifestyle and attitude. The proposed extended framework is validated using primary data (n = 314) collected from female respondents using a structured questionnaire; the partial least square-based structural equation modeling technique is subsequently used to test the proposed hypothesis.

The results show that effort expectancy, social influence, price value, habit, attitude and lifestyle have significant positive effects on women’s behavioral intention to use WHMT and accelerate actual usage behavior. Notably, effort expectancy and habit exhibit the largest impact on behavioral intention. However, performance expectancy, facilitating conditions and hedonic motivation are not significantly associated with behavioral intentions.

The findings of this study are important for healthcare practitioners and service providers to comprehensively understand the factors that affect women’s behavioral intentions in line with their actual usage behavior. This insight will help policymakers design viable strategies regarding WHMT to promote its sustainable usage in least developed countries.

This study contributes novelty by using an extended model that links women’s attitudes and lifestyles to their adoption of WHMT. This study also fills the gaps in the existing literature on women’s behavioral intentions in the context of WHMT by showing novel associations in the domain of WHMT uptake.

Both the SEC (Securities and Exchange Commission) and the popular press have routinely criticized firms for the complexity of their financial disclosures. This study aims to investigate how financial analysts respond to the tone complexity of firm disclosures.

Using approximately 20,000 earnings conference call transcripts of S&P 1,500 firms between 2005 and 2015, the authors first calculate the abnormal negative tone, the measure of tone complexity; then use such tone measure in econometric models to examine analyst forecast behavior. The authors also test the robustness of the results under different model specifications, tone word lists and alternative tone measure calculations.

Consistent with the notion that analysts respond to the information demand from investors and incur more costs and effort to analyze firm disclosure when the tone is more complex, the authors find that higher tone complexity is positively and significantly associated with more analyst following, longer report duration, more forecast revisions, larger forecast error and larger forecast dispersion. In addition, the authors find that tone complexity has a long-term impact on analyst following but has a limited long-term impact on analyst report duration, analyst revision, forecast error and dispersion.

This study complements existing literature by highlighting the information role of financial analysts and by providing evidence that analysts incorporate the management tone disclosed during conference calls to adjust their forecasting behaviors. The results can be used by policymakers as evidence and support for further improving firm communication from a new dimension of disclosure tone.

The purpose of this paper is to focus on managing the risk of fraud in commercial letters of credit (LC) in Bangladesh involving three parties: the seller, the buyer and the bank. It addresses the severity of LC fraud, the banks’ actions when detected and the preventive measures the relevant parties can adopt.

This research uses doctrinal and qualitative methods to propose strategic actions that benefit buyers, sellers, banks, legal professionals and judges. The study aims to explore the modus operandi used by fraudsters through thematic analysis.

The study’s findings reveal that LC fraud has escalated to a concerning level, posing a significant threat to the economic stability of Bangladesh. Measures must be taken to mitigate this risk and safeguard the country’s financial integrity. To effectively combat the risk of LC fraud, the updated version of UCP must include specific and detailed guidelines on LC fraud. This study recommends preventative measures that all parties involved must take to reduce the likelihood of fraud significantly.

Due to a lack of LC experts, the participant sample for the study in Bangladesh was limited. Nevertheless, most banking participants were highly distinguished and held the Head of Trade Finance Department position in commercial banks. A few academics and legal practitioners with LC expertise also participated in the study.

It provides cutting-edge solutions to effectively handle LC fraud risk and provides proactive measures to prevent it.

Generative pretrained transformers (GPTs), soaring to one million users at lightning speed, outpaced social media giants (15 times faster) (Buchholz, 2023). Despite this, scant research explored GPT’s impact on the digital entrepreneurial intentions (EIs) of students and tech-savvy generations. This study aims to pioneer a fusion of the technology acceptance model (TAM) and the theory of planned behavior (TPB), bridging the gap in research.

In this bold quantitative quest, business administration students became fearless participants, engaging in a survey of profound significance. Guided by the mighty powers of G*Power and Stata’s structural equation modeling builder, the intricate relationships within a robust sample of (n = 400) were unraveled.

The mediating power of GPT usefulness and GPT ease of use part of the TAM emerges, paving the way for a future brimming with digital entrepreneurship (DE) boundless possibilities. Predictably, the study found that TPB constructs also affect the EI of students.

This groundbreaking study brings together the powerful combination of TAM and TPB, while pioneering the exploration of GPT models’ mediating role. Its findings offer invaluable contributions to the field of DE and policymakers.

The purpose of this study is to analyze the fluctuations in gold prices within the Saudi Arabian market and to develop a reliable forecasting model to aid market participants and policymakers in making informed decisions.

In this study, we employ a rigorous time series analysis methodology, including the ARIMA (Auto Regressive Integrated Moving Average) model, to analyze historical gold price data in the Saudi Arabian market. The approach involves identifying optimal model parameters and assessing forecast accuracy to provide actionable insights for market participants.

The study showcases that the autoregressive properties of past gold prices play a pivotal role in capturing the inherent serial correlation within the market, enabling the ARIMA model to effectively forecast future gold price movements with accuracy.

Our study primarily focuses on quantitative analysis, whereas few qualitative parameters are not included. Future studies may benefit from incorporating qualitative factors and expert opinions to enhance the robustness of gold price predictions and capture the full spectrum of market dynamics.

Participants and policymakers may find this study helpful in navigating the complicated Saudi Arabian gold market. By understanding financial stability and investment decisions more thoroughly, individuals and institutions may be able to manage their portfolios more effectively.

By combining historical insights with advanced ARIMA modeling techniques, this research provides valuable insight into gold price dynamics in the Saudi Arabian market.