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This paper aims to determine causal relationships between the level of productive credit, real deposits and money demand – all of them in real terms – and Gross National Product between 2006 and 2020.

The vector autoregressive technique (VAR) was used, where data from real macroeconomic aggregates published by the Central Bank of Ecuador (BCE) are correlated, such as productive credit, gross domestic product (GDP) per capita, deposits and money demand.

The results indicate that there is no causal relationship, in the Granger sense, between GDP and financial activity, but there is between the growth rate of real money demand per capita and the growth rate of total real deposits per capita.

The study shows that bank credit mainly finances the operations of current assets and/or liabilities. In addition, economic agents use the banking system mainly to carry out transactional and precautionary activities.

The measurement of women's economic welfare and exploring its underlying factors have been undervalued in the context of Khyber Pakhtunkhwa, Pakistan. This study addressed this gap by focusing on assessing women's subjective economic welfare and its socioeconomic and cultural determinants in the education and health sectors within Mardan, Northern Pakistan.

The study used stratified random sampling techniques for the selection of sample respondents and collected data through a well-structured questionnaire. To measure women’s economic welfare, the study utilizes Lorenz curves, the Gini index, the Sen Social Welfare function and an individual's gross monthly income. Furthermore, the ordinary least squares method was utilized to analyze the determinants of economic welfare.

The findings show greater income inequality and a lower welfare level for women in the education sector compared to the health sector. Likewise, the study identifies several key determinants, such as age, educational qualification, job experience, respect for working women, outside and work-place problems and the suffering of family members of working women for their economic well-being.

This study makes valuable contributions to the literature by focusing on the cultural perspective of Pakhtun women in Mardan and providing a context-specific understanding of subjective economic welfare. Additionally, the authors collected first-hand data, which gave an original outlook on working women's current economic welfare level. Furthermore, this study undertakes a comparative analysis of working women's welfare in the health and education sectors. This comparison offers a more accurate portrayal of the challenges and opportunities specific to these occupations.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0246

Extending the elaboration likelihood model (ELM), this study investigates the impact of social media influencers (SMI) on consumer well-being (CW) as well as the influence of CW on purchase intention.

A between-subjects experiment (macro- vs mega-influencer) was conducted to assess the proposed hypotheses. A total of 190 consumers participated in the experiment, and SmartPLS 3.3 was used for multigroup analyses.

Overall, argument quality (AQ), source's credibility (SC) and influencer's kindness positively predict CW, and CW predicts purchase intention. It was also found that SC is more important when information comes from a mega-influencer, whilst kindness is essential for a macro-influencer.

The results of this study imply that CW should be an essential component of influencer marketing strategy. Marketing managers should hire credible and kind influencers who can produce quality arguments. Additionally, the selection of SMI (macro- vs mega-influencer) should be aligned with the marketing objective and type of persuasion required.

This is one of the early attempts to extend ELM by introducing influencer kindness as a peripheral cue. Moreover, the study offers novelty by examining the effects of influencer characteristics (AQ, SC and kindness) on CW and comparing these effects across macro- and mega-influencers.

藉著擴展詳儘可能性模型, 本研究擬探討網絡紅人對消費者福祉的影響, 以及消費者福祉對購買意圖的影響。

研究人員進行被試間實驗 (中網紅對大型網紅) , 以對提出的假設進行評價。190名消費者參與實驗, 研究人員使用SmartPLS 3.3 進行多群組分析。

總的來說, 論點品質、來源可信度和網紅的仁慈體貼, 均能積極預測消費者福祉, 而消費者福祉亦可預測購買意圖。研究人員亦發現, 若資訊是來自大型網紅的話, 來源可信度則更形重要, 而對中網紅來說, 仁慈體貼則是不可或缺的。

研究結果暗示, 消費者福祉應是網紅市場營銷戰略的基本要素。市場經理應僱用可靠、仁慈體貼、並能提出優質論點的網紅。而且, 網絡紅人 (中網紅對大型網紅) 的挑選, 必須與營銷目標和說服的種類互相協調。

本研究為早期的嘗試, 利用引進網絡紅人的仁慈體貼作為周邊線索, 來擴展詳儘可能性模型。另外, 本研究探討網絡紅人的特徵 (論點品質、來源可信度和仁慈體貼) 會如何影響消費者福祉; 研究人員亦跨中網紅和大型網紅, 對這些影響進行比較, 就此而言, 本研究提供了創新的研究意念。

As sustainable performance has a central role in the small and medium enterprises (SMEs) performance literature, this study aims to examine the influence of networking capabilities in enhancing sustainable performance through knowledge workers’ productivity and digital innovation. It also examines the sequential mediating role of knowledge workers’ productivity and digital innovation on networking capabilities and SMEs’ sustainable performance relationship.

Data were collected from 308 knowledge workers in the information technology sector and analyzed using the Hayes Process Macro bootstrapping method to test the proposed hypotheses.

Results indicate that knowledge workers’ productivity and digital innovation individually and sequentially mediate the relationship between networking capabilities and SME’s sustainable (economic and environmental) performance, surprisingly, they do not act as a mediator between networking capability and SME’s social performance. SMEs should prioritize investments in the professional development of their knowledge workers through training and skill enhancement programs. This investment equips knowledge workers with the tools to effectively use the knowledge and resources acquired through networking. Thus, knowledge workers may improve performance by using these resources to tackle challenges.

Although this research focused on this specific context, it is prudent to acknowledge that additional factors may also exert influence on sustainable performance within SMEs, factors that managers may consider when making decisions. Methodologically, the cross-sectional design of this research poses a potential limitation, as it does not allow for the complete elimination of endogeneity concerns. However, it is worth noting that scholars have endorsed the use of cross-sectional data in cases where management researchers aim to expand beyond well-documented and longitudinal data sets.

This research offers practical recommendations for SMEs to improve their sustainable performance through networking. SMEs should seek partnerships with complementary knowledge to improve operations and for other performance-oriented benefits.

This study adds significantly to the literature on sustainable SME performance by studying the interdependent effects of networking capabilities. It also represents the individual and sequential mediation mechanism that links networking capabilities to SME success through knowledge worker productivity and digital innovation.

The purpose of this paper is to identify the factors determining students’ attitude toward using newly emerged artificial intelligence (AI) tool, Chat Generative Pre-Trained Transformer (ChatGPT), for educational and learning purpose based on technology acceptance model.

The recommended model was empirically tested with partial least squares structural equation modeling using 375 student survey responses.

The study revealed that students have a favorable view of the instructional use of ChatGPT. Usefulness, social presence and legitimacy of the tool, as well as enjoyment and motivation, contribute to a favorable attitude toward using this tool in a learning environment. However, perceived ease of use was not found to be a significant determinant in the adoption and utilization of ChatGPT by the students.

This research is intended to benefit enterprises, academic institutions and the global community by offering light on how students perceive the ChatGPT service in an educational setting. Furthermore, the application enhances confidence and interest among learners, leading to improved literacy and general awareness. Eventually, the outcome of this research will help AI developers to improve their product and service delivery, as well as benefit regulators in regulating the usage of AI-based bots.

Due to its novelty, the current research on AI-based ChatGPT usage in the education sector is rather restricted. This study provides the adoption aspects of ChatGPT, a new AI-based technology for students, thereby contributing significantly to the existing research on the adoption of advanced education technologies. In addition, the literature lacks research on the adoption of ChatGPT by students for educational purposes; this study addresses this gap by identifying adoption determinants of ChatGPT in education.

This paper aims to examine the effects of oil price uncertainty on corporate investment of Islamic stocks during the COVID-19 pandemic.

The study uses a panel data set that covers 398 listed Islamic stocks from seven major Asia Pacific countries over the period of five years from 2017 to 2021, yielding 1,990 observations. Specifically, this paper investigates the said association by combining the real options theory regarding investment and the panel data-based econometric method that captures the dynamic relationship, the generalized method of moments estimators.

The findings show that the relationship between the oil price volatility and corporate investment of Islamic stocks is significant and nonlinear in nature, suggesting the presence of both the growth options and the waiting options. Overall, the results reveal that corporate investment of Islamic stocks is hindered during the unprecedented corona crash, when oil price increases at exponential rates.

The findings suggest that considering the information caused by unprecedented events like the COVID-19 pandemic is crucial for investment decisions of Islamic stocks. Therefore, policymakers and regulators should incorporate the impact of oil price uncertainties caused by unprecedented events like the COVID-19 pandemic on firm’s investment expansion and diversification strategies.

To the best of the authors’ knowledge, this paper is the first to examine the relationship between the investment of Islamic stocks and the oil price uncertainty under compound options theory in top Asian oil-importing countries.

The present study aims to investigate the impact of corporate governance proxies by ownership structure and firm-specific characteristics, i.e. firm size, leverage, growth opportunities, previous year dividend, firm risk, profitability, and liquidity on dividend behavior of the Pakistan Stock Exchange (PSX) listed firms.

Final sample of the study consists of 140 PSX-listed firms. The study covers a period of six years, starting from 2015 to 2020. Dividend payout dummy, dividend payout ratio, and dividend yield were used to assess the dividend behavior of the sample firms. The appropriate regression procedures (logistic, probit, ordinary least square (OLS), and fixed effect regression) are used to test the study hypothesis. To check the robustness of the result, a system GMM estimation technique is also used in the present study.

The study reveals that institutional ownership, foreign ownership, and individual ownership have a significant positive whereas managerial ownership has a significant negative impact on the dividend decision of sample firms. Among firm-specific characteristics, it was found that liquidity, profitability, and the previous year's dividend were significantly positive, while growth opportunities were significantly inversely associated with dividend payout decisions of PSX-listed firms.

This study sheds light on the relationship between dividend policy, ownership structure, and firm-specific factors in the context of an emerging market like Pakistan. The study's findings have important implications for managers, minority shareholders, lawmakers, and investors looking for guidance on the dividend policy of publicly-traded non-financial firms.

The literature lacks studies that together analyze the ownership characteristics and firm-specific variables on dividend decisions, particularly in the context of developing economies. The current study aims to fill this gap.

This study aims to investigate the effect of monetary policy on housing prices for US economy. It specifically examines whether nominal or real interest rates are the key drivers behind fluctuations in housing prices in US.

Monthly data from January 1991 to July 2023 and various appropriate analytical tools such as unit root tests, Johansen’s cointegration test, vector error correction model (VECM), impulse response function and Granger causality test were applied for the data analysis.

The Johansen cointegration findings reveal the presence of a long-term relationship among the variables. VECM results indicate a negative correlation between nominal and real interest rates and housing prices in both the short and long terms, suggesting that a strict monetary policy can help in controlling the housing price increase in the USA. However, housing prices are more responsive to changes in nominal interest rates than to real interest rates. Additionally, the study reveals that the COVID-19 pandemic contributed to the upsurge in housing prices in the USA.

This study contributes by examining the role that nominal or real interest rates play in shaping housing prices in the USA. Moreover, given the recent significant upsurge in housing prices, this study presents a unique opportunity to investigate whether these price increases are influenced by the Federal Reserve's monetary policy decisions regarding nominal or real interest rates. Additionally, using monthly data, this study provides a deeper understanding of the fluctuations in housing prices and their connection to monetary policy tools.

The efforts of researchers in the 21st century have been devoted to developing novel approaches to leave planet earth green for future generations. This study aims to report the synthesis of microcapsules from natural essential peppermint oil and their application to a bleached polyester and cotton (PC) blended fabric.

Microcapsules were prepared by a complex coacervation process and applied through the conventional pad-dry-cure method. The liquid suspension of the microcapsules was examined by optical microscopy to investigate the surface morphology of the microcapsules. Scanning electron microscopy was used to examine the surface morphology of the fabric after the application of the microcapsules. The finished fabric was checked for its mosquito repellent activity at the lab scale using a standard test protocol (cage test) by inserting a human arm and hand enfolded with microcapsules treated fabric.

PC fabric treated with 6% microencapsulated peppermint oil at zero wash showed 95.3% repellency against mosquitoes, and after 30 washes, the repellency was 85.8% which confirmed the durability of the developed finished fabric. The finished samples exhibited excellent air permeabilities and absorbencies.

This study successfully developed peppermint oil microencapsulated fabric with excellent efficacy against three mosquito species.

The Sustainable Development Goal (SDG) 16 outlines sustainability as associated with peace, good governance and justice. The perception of international tourists about security measures and risks is a key factor affecting destination choices, tourist flow and overall satisfaction. Thus, we investigate the impact of armed forces personnel, prices, economic stability, financial development and infrastructure on tourism.

This research used data from 130 countries from 1995 to 2019, which were divided into four income groups. This study employs a two-step generalized method of moments (GMM) technique and a novel tourism index comprising five relevant indicators of tourism.

A 1% increase in armed forces personnel expands tourism in all income groups – 0.369% High Income Countries (HICs), 0.348% Upper Middle Income Countries (UMICs), 0.247% Lower Middle Income Countries (LMICs) and 0.139% Low Income Countries (LICs). The size of the tourism-safety coefficient decreases from high to low-income groups. The impact of inflation is significantly negative in all panels, excluding LICs. The reduction in tourism was 0.033% in HICs, 0.049% in UMICs and 0.029% in LMICs for a 1% increase in prices. The increase in the global tourism index is more in LICs (0.055%), followed by LMICs (0.024%), UMICs (0.009%) and HICs (0.004%) for a 1% expansion in the gross domestic product (GDP)/capita growth. However, the magnitude of the growth-led tourism impact is greater in developing countries. A positive impact of foreign direct investment (FDI) inflow was found in all panels like 0.016% in HICs, 0.050% in UMICs and 0.119% in LMICs for a 1% increase in FDI inflow. The rise in the global tourism index is 0.097% (HICs), 0.124% (UMICs) and 0.310% (LMICs) for a 1% rise in the financial development index. The increase in the global tourism index is 0.487% (HICs), 0.420% (UMICs) and 0.136% (LICs) for a 1% rise in the infrastructure index.

Empirical analysis infers important policy implications such as (a) establishment of a peaceful environment via recruitment of security personnel, use of safe city cameras, modern technology and law enforcement; (b) provision of basic facilities to tourists like sanitation, drinking water, electricity, accommodation, quality food, fuel and communication network and (c) price stability through different tools of monetary and fiscal policy.

First, it explains the effect of security personnel on a comprehensive index of tourism instead of a single variable of tourism. Second, it captures the importance of economic stability (i.e., economic growth, financial development and FDI inflow) in the tourism–peace nexus.