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This paper aims to investigate the use of big data (BDU) in predicting technological innovation, supply chain and SMEs' performance and whether technological innovation mediates the association between BDU and firm performance. Additionally, this research also seeks to explore the moderating effect of information sharing in the association between BDU and technological innovation.

Using survey methods and structural associations in AMOS 24.0., the proposed model was tested on SME managers recruited from the largest economic and manufacturing hub of China, Pearl River Delta.

The findings suggest that BDU is positively related to technological innovation (product and process) and organizational outcomes (e.g., supply chain and SMEs performance). Technological innovation (i.e., product and process) significantly mediates the association between BDU and organizational outcomes. Moreover, information sharing positively moderates the association between BDU and technological innovations.

This research provides deeper insights into how BDU is useful for SME managers in achieving the firm’s goals. Particularly, SME managers can bring technological innovation into their business processes, overcome the challenges of forecasting, and generate dynamic capabilities for attaining the best SMEs’ performance. Additionally, BDU with information sharing enables SMEs reduce their risk and decrease production costs in their manufacturing process.

Firms always need to adopt new ways to enhance their productivity using available resources. This is the first study that contributes to big data and performance management literature by exploring the moderating and mediation mechanism of information sharing and technological innovation respectively using RBVT. The study and research model enhances our insights on BDU, information sharing, and technological innovation as valuable resources for organizations to improve supply chain performance, which subsequently increases SME productivity. This gap was overlooked by previous researchers in the domain of big data.

Since 2004, Pakistan carried the banking sector under the umbrella of the Islamic financial paradigm, consequently the Islamic Banking Industry (IBI) placed an upright position in the banking and financial market of Pakistan. In this context, this study aims to analyze the effect of Shariah-compliant products of the IBI on the pace of economic growth in Pakistan.

The data set covered 13 Pakistani Islamic banks comprising four full-fledged Islamic banks and 9 conventional banks holdings standalone Islamic Branches, for the period 2004–2019. Considering nature of the empirical model and data set, the estimation was carried out with the Pooled Ordinary least squares estimation technique.

The findings of the study reveal that Shariah-compliant products have a positive effect on the pace of economic growth. This indication is based on the fact that five out of six Shariah-compliant products hold positive signs and are statistically significant. In addition, the empirical evidence shows that at large conventional financial sector signifies its role in the Shariah-compliant products and pace of economic growth nexus. Among the control variables, foreign direct investment, human capital, trade openness, inflation and private credit pose negative, whereas money supply and stock market capitalization have a positive effect on the pace of economic growth in Pakistan. Findings of the study points towards the fact that Shariah-compliant financing has great potential to enhance the economic growth of Pakistan therefore to touch the sustainable development goals (SDGs).

Having played a significant role in the growth process, Islamic bankers should portray a positive image of their industry to the government authorities. The government should design a public policy to encourage Islamic modes of finance at a macro level to increase the pace of economic growth and therefore SDGs realization.

Findings of the study present new insight into the application of Shariah-compliant products of IBI toward the realization of SDGs in case of Pakistan.

This study delves into the significant impact of entrepreneurial leadership (EL), organizational innovation (OI) and competitive advantage (CA) on the sustainability of organizations in the Jordanian Islamic insurance sector. This study aims to unravel the intricate dynamics among these pivotal factors, highlighting their collective role in the sustained success and resilience of Islamic insurance companies in Jordan. By examining the synergistic interplay between EL, OI and CA, the research seeks to offer invaluable insights into cultivating a sustainable future for these institutions.

This study adopts a positivist, quantitative approach to investigate the relationships between EL, OI, CA and organizational sustainability (OS). Data collection was achieved through questionnaires in a cross-sectional analysis of these variables. Convenience sampling yielded 389 valid responses, an 81% response rate, ensuring a thorough understanding of the dynamics within this framework.

The findings from the study on Jordanian Islamic insurance companies highlight the critical role of EL in influencing organizational dynamics. The research establishes a positive relationship between EL and both OI and OS. Furthermore, it identifies a significant positive correlation between EL and CA, underscoring the importance of leadership in securing a competitive edge. This study also emphasizes the vital role of OI in bolstering OS and confirms that a strong CA enhances OS, reinforcing the interconnectivity of these essential organizational components.

This study provides important insights for professionals in the insurance sector, especially those within the Jordanian Islamic insurance industry. It emphasizes the crucial role of EL in fostering innovation, competitiveness and sustainability. By understanding the significance of EL, companies can adopt more dynamic and effective strategies in leadership, organizational development and strategic planning, thereby enhancing their overall performance and resilience.

By conceptualizing EL and OS as second-order constructs, this study seeks to provide a detailed understanding of how EL catalyzes OI, CA and sustainability in an organizational context. This approach contributes significantly to the broader discussion on EL and OS, particularly within the Arab region and specifically in Jordan, offering nuanced insights into their roles and interrelations in shaping organizational dynamics.

The study aims to explore factors that influence students’ academic performance in the context of physical ergonomics and assess the mediating effect of motivation between lighting, noise, temperature, chair design and students’ performance from the student’s perspective.

The research was categorised as a correlational study and employed non-contrived and cross-sectional methods to achieve its objectives. The target population was university students aged 18 years old and above enrolled in Malaysia’s higher education institutions. Due to the inaccessibility of the sample frame, convenience sampling, a type of non-probability sampling, was utilised. Data collection was conducted through an online survey primarily distributed among student groups.

The study’s findings reveal that only two exogenous variables, lighting and noise, directly influence students' performance. Additionally, motivation is a potent and significant factor in shaping students' performance. Motivation is also identified as a mediator in the complex relationship between lighting, noise, temperature and student performance. Surprisingly, although temperature does not directly influence student performance, it indirectly influences performance through motivation.

This study is an original exploration into the intricate factors shaping students' academic performance within the domain of physical ergonomics from a student perspective. The research uniquely investigates the mediating impact of motivation on the relationships between lighting, noise, temperature, seating arrangements and academic outcomes. The findings will contribute novel insights to the existing body of knowledge, offering a distinct perspective on the complex dynamics that influence student learning experiences and performance in educational settings.

The abrupt outbreak of coronavirus disease (COVID-19) hit every nation in 2020–2021, causing a worldwide pandemic. The worldwide COVID-19 epidemic, described as a “black swan”, has severely disrupted manufacturing firms' supply chain. The purpose of this study is to investigate how supply chain data analytics enable the effective deployment of agility, adaptability and alignment (3As) strategies, resulting in improving post-COVID disruption performance. It also analyses the indirect effect of supply chain data analytics on disruption performance through the 3As supply chain strategies.

The hypothesis and theoretical framework were tested using a questionnaire survey. The authors employed structural equation modelling through the SMART PLS version 3.2.7 to analyse data from 163 textile firms located in Pakistan.

The results revealed that the supply chain data analytics contributed positively and significantly to the agility and adaptability, while all 3As supply chain strategies impacted the PPERF substantially. Further, the connection between supply chain data analytics (SCDA) and disruption performance has substantially been influenced through 3As supply chain strategies.

The results imply that in the event of low likelihood, high effect disruptions, managers and decision-makers should focus their efforts on integrating data analytics capabilities with 3As supply chain policies to ensure long-term company success.

This research sheds fresh light on the importance of data analytics in effectively implementing 3As strategies for sustaining company performance amid COVID-19 disruptions.

This paper aims to propose a novel approach which integrates collocations and domain concepts for Chinese cosmetic word of mouth (WOM) sentiment classification. Most sentiment analysis works by collecting sentiment scores from each unigram or bigram. However, not every unigram or bigram in a WOM document contains sentiments. Chinese collocations consist of the main sentiments of WOM. This paper reduces the complexity of the document dimensionality and makes an improvement for sentiment classification.

This paper builds two contextual lexicons for feature words and sentiment words, respectively. Based on these contextual lexicons, this paper uses the techniques of associated rules and mutual information to build possible Chinese collocation sets. This paper applies preference vector modelling as the vector representation approach to catch the relationship between Chinese collocations and their associated concepts.

This paper compares the proposed preference vector models with benchmarks, using three classification techniques (i.e. support vector machine, J48 decision tree and multilayer perceptron). According to the experimental results, the proposed models outperform all benchmarks evaluated by the criterion of accuracy.

This paper focuses on Chinese collocations and proposes a novel research approach for sentiment classification. The Chinese collocations used in this paper are adaptable to the content and domains. Finally, this paper integrates collocations with the preference vector modelling approach, which not only achieves a better sentiment classification performance for Chinese WOM documents but also avoids the curse of dimensionality.

This paper aims to equip professionals and researchers in the fields of advertising, branding, public relations, marketing communications, social media analytics and marketing with a simple, effective and dynamic means of evaluating consumer behavioural sentiments and engagement through Arabic language and script, in vivo.

Using quantitative and qualitative situational linguistic analyses of Classical Arabic, found in Quranic and religious texts scripts; Modern Standard Arabic, which is commonly used in formal Arabic channels; and dialectical Arabic, which varies hugely from one Arabic country to another: this study analyses rich marketing and consumer messages (tweets) – as a basis for developing an Arabic language social media methodological tool.

Despite the popularity of Arabic language communication on social media platforms across geographies, currently, comprehensive language processing toolkits for analysing Arabic social media conversations have limitations and require further development. Furthermore, due to its unique morphology, developing text understanding capabilities specific to the Arabic language poses challenges.

This study demonstrates the application and effectiveness of the proposed methodology on a random sample of Twitter data from Arabic-speaking regions. Furthermore, as Arabic is the language of Islam, the study is of particular importance to Islamic and Muslim geographies, markets and marketing.

The findings suggest that the proposed methodology has a wider potential beyond the data set and health-care sector analysed, and therefore, can be applied to further markets, social media platforms and consumer segments.

To remedy these gaps, this study presents a new methodology and analytical approach to investigating Arabic language social media conversations, which brings together a multidisciplinary knowledge of technology, data science and marketing communications.

This paper aims to propose an approach for hate speech detection against politicians in Arabic community on social media (e.g. Youtube). In the literature, similar works have been presented for other languages such as English. However, to the best of the authors’ knowledge, not much work has been conducted in the Arabic language.

This approach uses both classical algorithms of classification and deep learning algorithms. For the classical algorithms, the authors use Gaussian NB (GNB), Logistic Regression (LR), Random Forest (RF), SGD Classifier (SGD) and Linear SVC (LSVC). For the deep learning classification, four different algorithms (convolutional neural network (CNN), multilayer perceptron (MLP), long- or short-term memory (LSTM) and bi-directional long- or short-term memory (Bi-LSTM) are applied. For extracting features, the authors use both Word2vec and FastText with their two implementations, namely, Skip Gram (SG) and Continuous Bag of Word (CBOW).

Simulation results demonstrate the best performance of LSVC, BiLSTM and MLP achieving an accuracy up to 91%, when it is associated to SG model. The results are also shown that the classification that has been done on balanced corpus are more accurate than those done on unbalanced corpus.

The principal originality of this paper is to construct a new hate speech corpus (Arabic_fr_en) which was annotated by three different annotators. This corpus contains the three languages used by Arabic people being Arabic, French and English. For Arabic, the corpus contains both script Arabic and Arabizi (i.e. Arabic words written with Latin letters). Another originality is to rely on both shallow and deep leaning classification by using different model for extraction features such as Word2vec and FastText with their two implementation SG and CBOW.

In this age of digitalization, organizations are generating large data from the organizations' manufacturing processes that are valuable for capturing a competitive edge. Chinese small and medium enterprises (SMEs) can bring organizations radical innovation by investing in innovation projects (i.e. big data use; BDU) using the SMEs' scarce resources. Thus, the authors' research aims to predict Chinese SMEs' radical innovation (RI) through BDU using the theoretical lenses of the resource-based view. Moreover, the authors' study also pursues to realize the undermining mediating process of business strategy alignment (BSA) and the buffering role of information sharing in BDU–RI linkage.

In total, 297 Chinese SMEs entrepreneurs and managers were recruited from the Yangtze River Economic Belt, China using a survey approach. In Mplus 7.4, the authors tested the proposed hypotheses.

The sample of 297 shows that BDU is directly and indirectly (via BSA) related to RI. Further, information sharing moderates the linkage between BDU and BSA and BSA and RI. The association between BSA and RI is only significant and stronger when information sharing is high.

This research is beneficial for SME entrepreneurs/managers to enhance the understanding of BDU, eliminate challenges of BSA via BDU and align business strategies to bring RI to Chinese SMEs.

SMEs always search for new ways to enhance SMEs' productivity using scarce resources. This is the first research that advances big data and innovation literature by predicting firm RI through BDU using a resource-based view. Moreover, this study is novel because the study investigates the mediation role of BSA and the moderating role of information sharing in the linkage between BDU and firm RI in Chinese SMEs.