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This study investigates the impact of traditional marketing analytics and big data analytics on the success of a new product. Moreover, it assesses the mediating effects of the quality of big data system.

This study is based on primary data that were collected through an online questionnaire survey from large manufacturing firms operating in UAE. Out of total distributed 421 samples, 327 samples were used for final data analysis. The survey was conducted from March–April 2020, and data analysis was done via Structural Equation Modelling (SEM-PLS).

It emerges that big data analysis (BDA), traditional marketing analysis (TMA) and big data system quality (BDSQ) are significant determinants of new product development (NPD) success. Meanwhile, the BDA and TMA significantly affect the BDSQ. Results of the mediating role of BDSQ in the relationship between the BDA and NPD, as well as TMA and NPD, are significant.

There are significant policy implications for practitioners and researchers concerning the role of analytics, particularly big data analytics and big data system quality, when attempting to achieve success in developing new products.

This is an original study based on primary data from UAE.

The aim of the study is to examine the impact of the big data analytics capabilities (BDAC) on the organizational performance. The study also examines the mediating role of ambidexterity and the moderating role of business value of big data (BVBD) analytics in the relationship between the big data analytics capabilities and the organizational performance.

This study collected primary data based on a questionnaire survey among the large manufacturing firms operating in UAE. A total of 650 questionnaires were distributed among the manufacturing firms and 295 samples were used for final data analysis. The survey was conducted from September to November in 2019, and data were analyzed based on partial least squares structural equation modeling (PLS-SEM).

The big data analysis (BDA) scalability is supported by the findings on the performance of firm and its determinants such as system, value of business and quality of information. The roles of business value as a moderator and ambidexterity as mediator are found significant. The results reveal that there is a need for managers to consider the business value and quality dynamics as crucial strategic objectives to achieve high performance of the firm.

The study has significant policy implication for practitioners and researchers for understanding the issues related to big data analytics.

This is an original study based on primary data from UAE manufacturing firms.

The aim of this study is to estimate the relationship between technological innovation and corporate environmental performance among energy companies working in Egypt.

The study extended the aim with the intention to assess the role of green financing in enhancing corporate environmental performance. Partial least squares (PLS)-based structural equation modeling (SEM) is applied to estimate the nexus among study variables.

The results indicated that technological innovation influenced environmental performance and has a positive impact on company performance. The role of green financing for environmental performance is also significant and positive. Moreover, corporate social responsibility (CSR) has insignificant role in environmental performance of the energy companies in the study context.

The study offers a valuable model for general managers of manufacturing organizations and policymakers to manage CSR, environmental strategy and green innovation in examining environmental performance. It can help to assist general managers of large manufacturing organizations to strengthen their internal resources like CSR, environmental strategy and green innovation to enhance environmental performance.

The findings of this article will help the practitioners to design policies regarding sustainable energy systems and green finance in the presence of any natural calamity.

This study primarily complements the existing literature by establishing how green financing and CSR can augment and/or interact between technological innovation and corporate environmental performance under COVID-19 crises, in a developing country.

Objective of the study is to determine the factors effecting successful women leadership and to draw a comparison between Thailand and Malaysia. Primary data were collected from both countries. Quantitative research approach was used in this study by using a cross-sectional research design. Population of the study was based on the textile companies of Thailand and Malaysia. Employees of textile companies were selected as the respondents. Partial Least Square (PLS) was used for data analysis. Results of the study found that learning autonomy, emotional intelligence, political differences and organization culture (OC) are the major factor which effect on successful women leadership in both countries. Moreover, in both countries, learning autonomy, emotional intelligence and OC have positive role in successful women leadership, however, political differences effect negatively on successful women leadership. Additionally, there is a difference between both countries, in case of moderating role of political differences and OC. In Thailand, OC has positive role to enhance women leadership through emotional intelligence. In case of Malaysia, political differences weaken the positive relationship between the emotional intelligence and successful women leadership.

This study aims to examine the necessity effects of big data analytics capabilities (BDAC) on decision-making performance (DMP), particularly in the public sector.

The authors used the combined methods of partial least square structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) to test the hypothesized relationships.

The findings show that the presence of all three BDAC (infrastructure, management and personnel) is significant and necessary to achieve higher levels of DMP. Specifically, the results revealed big data management capabilities to be of higher necessity to achieve the highest possible DMP. The findings provide public-sector practitioners with insights to support the development of their BDAC.

Time-sensitive domains such as the public sector require insight and quality decision-making to create public value and achieve competitive advantage. This study examined BDAC in light of the combined methods of (PLS-SEM) and NCA to test the hypothesized relationships in the public sector context.

The current work sought to investigate the mediating effect of supply chain ambidexterity on the relationship between Industry 4.0 capabilities and operational performance of manufacturing firms in Jordan.

Data collection was carried out through a survey with 253 respondents from manufacturing firms in Jordan through the first quarter in 2023. The quantitative approach and structural equation modeling (SEM) were applied to analyze the collected data. Dynamic capabilities view (DCV) theory was the adopted theoretical lens for this work.

The results demonstrated that Industry 4.0 capabilities positively and significantly affect exploration, exploitation and operational performance. In addition, the results confirmed that exploration and exploitation positively and significantly affect operational performance. Further, it is also found that exploration and exploitation in the supply chain positively and significantly mediate the relationship between Industry 4.0 capabilities and operational performance.

This study focuses on this gap to deepen the understanding of operational performance in a recent manufacturing environment under various factors and perspectives (Industry 4.0 capabilities and supply chain ambidexterity).

The purpose of the study is to identify how knowledge management processes impact innovation performance in the Jordanian medical sector (private hospitals) as well as identify how big data analytics moderates this performance.

Two hundred ninety-one questionnaires were analyzed for the purpose of this study. A structural equation model (SEM) was used to test convergence validity, discriminant validity and reliability. In order to analyze the data, bootstrapping was used.

The empirical results showed that all knowledge management processes are statistically significant in influencing innovation performance. Furthermore, big data analytics moderates the relationship between knowledge management processes and innovation performance.

The results of this cross-sectional study are limited to one country and one industry due to methodological limitations, and the results represent a snapshot at a particular point in time.

Jordan's medical leaders will benefit from this study, since it emphasizes the importance of knowledge management processes to enhance innovation performance, especially given the importance of big data analytics in the field, increasing innovation capabilities in the medical field, thereby increasing innovation levels.

The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.

This study attempts to assess the readiness of Saudi students for independent/autonomous learning, with a focus on learning of English as a Foreign Language (EFL). The study used a questionnaire and semi-structured interviews to gain insights from a population of 319 students (aged 15-24) about their perceptions of responsibilities, decision-making abilities, motivation, involvement in autonomy-related activities, and capacity to take charge of their own learning. The findings of the study confirmed the relatively low readiness of Saudi EFL learners for independent learning (M = 3.06 on a scale of 1 to 5, SD =.31). Learners demonstrated low responsibility levels, since only 17.27% of them perceived that they accept sole responsibility for their EFL learning. Respondents reported a moderate level of ability (M = 3.63) and motivation (M = 3.70) to learn English. A considerable percentage of participants (27.29%) reported that they are rarely involved in self-directed activities; they demonstrated high levels of teacher dependency and low levels of learner independence. Despite the participants’ reasonable level of awareness of the nature of learner autonomy and its demands, their responses identified them as EFL learners with low autonomy. This study informs EFL learning stakeholders in Saudi Arabia that learners’ readiness for such conditions must be developed before interventions aimed at promoting autonomy are implemented in this context.

.هيتاذ ةروصب ةيزيلجنلإا ةغللا ملعتل نييدوعسلا بلاطلا ةيزهاج ىدم ميقت نأ ةساردلا هذه لواحت تفظونايبتسا ةساردلا ا تلاباقمو رظن ةهجو ىلع لوصحلل319 لوح ابلاط يتاذ لكشب ملعتلل ةيلوئسملا مهلمحت ىدمةيعفادلا ،رارقلا ذاختا ىلع مهتردق ، ةغللا ملعتلةيبنجلاا ةغللا ملعتل نييدوعسلا بلاطلا ةيزهاج فعض ةساردلا جئاتن تتبثا .يتاذ لكشب ملعتلا ىلع ةردقلاو ،ةيتاذلا ةطشنلأا يف ةكراشملا ،يلجنلإا = طسوتم( يتاذ لكشب ةيز3.06 = يرايعم فارحنا ،31. ثيح يتاذ لكشب ملعتلل ةيلوئسملل مهلمحت فعض نوكراشملا تبثا .)تبسن ام ىعداه طقف(17.27 ملعتلا ىلع ةردقلل ةطسوتم تايوتسم نوكراشملا سكع نيح يف كلذل مهلمحت نيكراشملا يلامجا نم )%3.63ةيعفادلاو ) لجنلإا ةغللا ملعتل( ةيزي3.70( نيكراشملا نم ةريبك ةبسن سكعت .)27.83يف مهتكراشم مدع )% يتاذلا ملعتلا ةطشنأ ريبك لكشب دامتعلااولوح نوكراشملا اهادبا يتلا ةطسوتملا تايوتسملا نم مغرلا ىلع .سفنلا ىلع دامتعلاا فعضو ملعملا ىلع فارتعلاا بمهتاباجا نا لاا هتابلطتمو يتاذلا ملعتلا ةيمهأ هذه يصوت .يتاذلا ملعتلل مهتيزهاج مدع تتبثا ةلباقملا ةلئساو نايبتسلاا ىلعلا جمد ةلواحم لبق يتاذلا ملعتلل ةبسانملا ةئيبلا ريفوتب ةيدوعسلا ةيبرعلا ةكلمملا يف ةيزيلجنلإا ةغللا ملعت نع نيلوئسملا ةساردلا يف بلاط .ملعتلا نم عونلا اذه

Digital technology is increasingly important in enhancing organizational agility (OA). Institutional theory and resource-based view were harmonized to analyze firms' adoption of digital technologies. However, previous studies on OA have revealed that external pressures imply the imposition of barriers or technological standards that ultimately restrict OA. This study employs this double theoretical lens to investigate the mediation role of business analytics capability (BAC) in the relationship between co-innovation (CO), i.e. open innovation in digital platforms, and OA, as well as the negative moderating effect of external pressure for artificial intelligence adoption (EPAIA) on this mediation.

Structural equation modeling was used to test the moderated mediation with survey data from 229 firms.

The main result indicates that 72% of OA variance is explained by the effect of CO that is transmitted by the mediator (BAC). However, contrary to the authors' expectations, EPAIA only has a positive moderating effect along the path between BAC and OA.

This work contradicts the prevalent notion of the negative consequences of external pressures for artificial intelligence adoption. Specifically, this study's findings refute the notion that institutional pressures are the source of technical problems that disrupt CO and BAC integration and reduce OA. In contrast, the unexpectedly positive effect of EPAIA may indicate that this type of external pressure can be viewed as a significant sign and an opportunity for the company to adopt the industry's most advanced and effective digital transformation practices.