Search results

The purpose of this chapter is to cover banking efficiency using the concept of the Meta frontier function and to study group and subgroup differences in the production technology. This study estimates the technical efficiency (TE) and technology gap ratios (TGRs) for banks in Islamic countries. Using the assumption of the convex hull of the Meta frontier production set using the virtual Meta frontier within the nonparametric approach as presented by Battese and Rao (2002), Battese et al. (2004), and O'Donnell et al. (2007, 2008) and after relaxing this assumption, the study investigates if there is a significant difference between these two methods. To overcome the deterministic criterion addressed to nonparametric approach, the bootstrapping technique has been applied. The first part of this chapter covers the analytical framework necessary for the definition of a Meta frontier function and its estimation using nonparametric data envelopment analysis (DEA) in the case where we impose the assumption of the convex production set and follows in the case of relaxation of this assumption. Then we estimated the TE and the TGR in concave and nonconcave Meta frontier cases by applying the Bootstrap-DEA approach. The empirical part will be reserved for highlighting these methods on data bank to study the technical and technological performance level and prove if there is a difference between the two methods. Three groups of banks namely commercial, investment, and Islamic banks in 17 Islamic countries over a period of 16 years between 1996 and 2011 are used.

This study aims to evaluate the adoption of four types of supplier sustainability risk management (SSRM) strategies, namely, risk avoidance (RA), risk acceptance (RAC), collaboration-based risk mitigation (CBM) and monitoring-based risk mitigation (MBM) in Sri Lankan apparel and retail industries, and to investigate their effect on supply chain performance (SCP).

This study uses the dynamic capability view (DCV) to develop its hypotheses. Data collected from 89 firms were analysed using partial least square (PLS) structural equation modelling and PLS-based multiple group analysis.

Sri Lankan apparel and retail firms adopt RA and MBM strategies relatively more than CBM and RAC strategies, whereas there is no significant difference between the two industries in terms of the use of SSRM strategies. The path analysis revealed significant effects of RA and RAC strategies on SCP of both industries. The effect of CBM strategy on SCP is moderated by industry, while MBM has no significant impact.

While managing supplier sustainability risks effectively, RA and RAC strategies provide more opportunities for managers to improve SCP. In achieving SCP, CBM strategies are proven to be more effective for retail industry compared with the apparel sector. Although MBM strategies offer sustainability advantages to firms, their contribution to improving the performance of apparel and retail supply chains is not significant. This research is limited to only two industries (apparel and retail) in Sri Lanka, where the evidence for the effects of SSRM strategies is not available for other contexts.

Either the effects of the four types of SSRM strategies on SCP or the moderating effect of industry on these effects have not been empirically confirmed in the literature. Evaluating the extent to which different strategies are implemented in Sri Lankan apparel and retail industries is another significant contribution of this research. Furthermore, this study contributes by using DCV to a sustainability-based supply chain risk management research.

This paper aims to identify the relevant contributing constructs of readiness for the implementation of intelligent contracts (iContracts) in the construction industry. This study investigates the relationship between the personality dimensions of technology readiness index (TRI) and the system specific factors of technology acceptance model (TAM) within the context of iContracts.

Drawing insights from the extant literature and the author's previous qualitative investigations into iContract readiness constructs, a quantitative approach is used to operationalise the constructs by offering relevant statements to be measured and validated through a multiple-item scale against the users intent to accept the future iContract technology.

This study confirms and validates the relationship of the proposed iContract readiness index (iCRI) statements against the established TAM factors by offering 18 new constructs influencing technology readiness of the iContract technology. This study proves 9 of the 12 hypotheses highlighting key factors to be addressed for the successful development of the iContract technology.

This paper contributes to the body of knowledge by proposing a novel iCRI that informs an iContract technology readiness acceptance model (iCTRAM) for a trending technology. The iCTRAM can guide developers in producing an appropriate iContract solution and assess the readiness of users and organisations for the successful adoption of the iContract concept.

This study offers a unique theoretical framework, in an embryonic field, for predicting the success of iContract implementation within construction organisations. This study combines the established studies of TRI and TAM in producing a predictive iContract readiness assessment tool.

Drawing on the literature on brand–consumer relations in an attempt to apply the concept of brand love to a city tourism destination, this paper aims to explore the antecedents and consequences of city brand love.

Based on quantitative research and cluster sampling, study data was collected from 330 international tourists who visited six Iran cities. The research model is tested using partial least square structural equation modeling.

According to the results, city brand attractiveness, city brand identification and memorable city brand experience are the antecedents of city brand love. Moreover, the city brand attractiveness, both directly and indirectly through memorable experiences and identification with the city brand, is one of the antecedents of city brand love. Finally, the results suggest that word of mouth and revisit intention are major behavioral outcomes of city brand love.

By providing a validated conceptual model that traces the antecedents and consequences of city brand love, this study attempts to answer prior calls for examination from the viewpoint of city tourism scholars.

本研究基于定量研究和整群抽样, 研究数据来自访问伊朗六个城市的330名国际游客。研究采用偏最小二乘结构方程模型对研究模型进行了检验。

借鉴品牌-消费者关系的相关文献, 本研究试图将品牌爱的概念应用于一个城市旅游目的地, 探讨城市品牌喜爱的前因和后果。

结果表明, 城市品牌吸引力、城市品牌辨识度和令人难忘的城市品牌体验是城市品牌喜爱的前因。城市品牌吸引力通过城市品牌直接或间接通过难忘体验和认同产生, 是城市喜爱的前因。最后, 研究结果表明, 口碑和重访意愿是城市品牌喜爱的主要行为结果。

通过提供一个经过验证的概念模型, 追溯城市品牌喜爱的前因后果, 本研究试图从城市旅游学者的角度回答先前检验的呼吁。

A partir de una investigación cuantitativa y muestreo por conglomerados, los datos del estudio se recogieron de 330 turistas internacionales que visitaron seis ciudades iraníes. El modelo conceptual se analizó mediante un modelo de ecuaciones estructurales de mínimos cuadrados parciales (PLS).

Basándose en la literatura sobre las relaciones entre marca y consumidor para aplicar el concepto de amor de marca a una ciudad como destino turístico, la presente investigación analiza los antecedentes y las consecuencias del amor de marca de ciudad.

Conforme a los resultados, el atractivo de la marca ciudad, la identificación con la marca ciudad y la experiencia memorable con la marca ciudad son los antecedentes del amor a la marca ciudad. Adicionalmente, el atractivo de la marca ciudad, tanto directa como indirectamente a través de las experiencias memorables y la identificación con la marca ciudad, es uno de los antecedentes del amor por la marca ciudad. Finalmente, los resultados sugieren que la comunicación boca-oído y la intención de volver a visitar la ciudad son los principales resultados comportamentales del amor de marca de ciudad.

Al proporcionar un modelo conceptual validado que analiza los antecedentes y las consecuencias del amor de marca de ciudad, este estudio trata de responder a las llamadas para su estudio desde la óptica del turismo urbano.

The purpose of this study is to gain empirical insights into whether accounting information systems (AIS) usage matters among Jordanian small and medium-sized enterprises (SMEs) during the period of COVID-19 pandemic.

The suggested research model in the current study is based on the extending technology acceptance model (TAM) to test the antecedents’ factors that impact on AIS usage among SMEs. To test the proposed research model, partial least squares structural equation modeling (PLS-SEM) was used.

The empirical findings revealed all postulated hypotheses were accepted except H3. Contrary to what is expected, the empirical outcomes confirmed that perceived compatibility does not affect the perceived usefulness of AIS, and hence, the related hypothesis was rejected.

The results of the current research could be beneficial to a number of managers (owners) to obtain a better understanding of the benefits of AIS success usage among Jordanian SMEs performance during crises time as the COVID-19 pandemic crisis.

The environment in which users acquire medical and health information has changed dramatically, with online health communities (OHCs) emerging as an essential means for accessing health information. It is imperative to comprehend the factors that shape the users' compliance willingness (UCW) to health information in OHCs.

This study adopted the information adoption model (IAM) and theory of planned behavior (TPB) to investigate the influence of argument quality (AQ), source credibility (SC) and subjective norms (SN) on UCW while considering the two types of online health information – mature and emerging treatments. The authors conducted an explanatory-predictive study based on a 2 (treatment types: mature vs. emerging) * 2 (AQ: high vs. low) * 2 (SC: high vs. low) scenario-based experiment, using the partial least squares structural equation modeling (PLS-SEM).

SC positively influences AQ. AQ, SC and SN contribute to information usefulness (IU). These factors positively affect UCW through the mediation of IU. SN were found to improve UCW directly. Moreover, the moderating effect of SC on AQ and IU was more substantial for emerging treatments.

The research model integrates IAM and TPB, considering information types as an additional variable. The approach and findings provide a valuable explanation for UCW to health information in OHCs.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

This project aims to examine how entrepreneurial orientation and succession planning among Palestinian family businesses positively affects their associated growth potential, considering the mediating role of innovation and the moderation effect of geographic location. Leveraging ethnic entrepreneurship theory, the authors compare these types of enterprises in the USA with their counterparts in Palestine.

This cross-sectional quantitative research analyzes data collected from October through December 2022. 180 Palestinian family-owned firms completed a survey; 90 companies were located in Palestine, while the other 90 were in the USA. Structural equation modeling analysis was conducted using Smart-PLS4. The interrelations of the conceptual framework were examined via path analysis and bootstrapping techniques.

The authors found a statistically significant positive effect of entrepreneurial orientation on Palestinian family business growth; the authors’ results concurrently indicated succession planning did not affect growth within the authors’ selected population. The authors also discovered innovation mediates the relationship between orientation and growth, and business location appears to moderate this relationship. The authors’ research indicates geography appears to favor Palestinian family-owned companies in the USA, where the authors found opportunity-driven immigrant entrepreneurs benefit from the structured business systems in a highly-developed country.

Given the current situation in Palestine, it is essential to understand the potential contribution that Palestinian family-owned businesses globally can make to reconstruct the country’s local economy. The next few years will be critical in figuring out how innovative thinking can boost the region’s recovery and increase Palestinian-based family companies’ ability to engage in sustainable entrepreneurship with reinvestment support from its diaspora. Therefore, it is important to have research that identifies factors that could improve these businesses’ continued performance and growth potential. This study also aids in further understanding the defining characteristics of Palestinian-owned family firms, enhancing general theories related to entrepreneurship among ethnic and diasporic groups.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.