Search results

Consumers may enjoy the information sharing and social support made available when a social media platform is used for pre-purchase research; however, do consumers reevaluate the privacy and security of the platform differently when ordering and payment capabilities are added? As social media systems have evolved into social commerce platforms (SCPs), individuals are often faced with whether to complete a purchase they have been researching or switch to a traditional e-commerce platform to complete the transaction. This research examines consumer trust formation in the SCP channel and how consumer interest and engagement in the channel are maintained and influence consumer decisions to purchase via the SCP.

Based on trust and involvement literature, a research model was conceptualized to capture consumer beliefs about SCP privacy and security and whether the SCP can be trusted, using these inputs into subsequent consumer interest, engagement and decisions on whether to use the SCP for purchasing. The research model was empirically tested using the panel data's structural equation modeling (AMOS) (n = 405). The data showed acceptable reliability and convergent validity, while the original research model provides predictive validity and theory-confirming insights.

Results confirm that consumer perceptions of privacy and security play a crucial role as decision criteria, informing their judgments of whether a new social commerce channel can be trusted enough to conduct purchases. Further, consumer trust supports their interest in the SCP, resulting in enduring and enhanced behavioral use and, to a lesser extent, purchase intent. Still, a majority of this sample declined to purchase using the SCP and rather preferred to transact on tried and trusted traditional e-commerce sites.

This study is among the first to examine trust formation in new SCPs, where consumers are deciding to expand their engagement level from social and informational to commercial.

The advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.

This study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.

The authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.

These results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.

To determine the factors influencing the perception of undergraduate students toward research–teaching nexus and also to examine its impact on the attitude of the students.

The study used a quantitative design to examine the perceptions of students in higher education institutions in India. Descriptive and inferential statistics were used to describe the data and test the hypothesis. The data was collected using a structured questionnaire for a sample of 188 students from higher education institutions (HEIs). Further, in line with the purpose of the study and to test the hypotheses, the study used descriptive statistics, exploratory factor analysis and multivariate regression analysis.

The study demonstrates that the factors influencing the perception of undergraduate students toward research teaching nexus were identified using principal component analysis (PCA) with varimax rotation, conducted on 27 measurement items. In addition, the results of the multivariate regression analysis indicated that research-based, research-tutored and research motivation dimensions, significantly impact the graduate students' perception of research–teaching nexus.

The outcomes of this research may become valid input to HEI regulators, researchers and teachers while framing the policies and implementation of the same about the pertinent issues discussed in the paper. Further, it contributes to the existing theory that teaching–research quality and level of motivation are also the predominant factors influencing the teaching–research nexus among students. In addition, the outcome of this paper also supports HEIs to achieve the United Nations Sustainable Development Goals (UNSDGs) at large.

This study contributes to the ongoing debate and scarce literature on research–teaching nexus at the higher education level. The factors derived in this paper propose a framework for aligning teaching and research in academic programs to create high-quality human resource in the nation.

This study aims to address the critical need for innovative financing solutions in the global housing sector, focusing specifically on Malaysia’s distinct housing finance system encompassing both conventional and Islamic loans. The primary objective is to develop a transformative housing finance model that addresses affordability challenges and reshapes the Malaysian housing landscape.

The study presents an alternate housing finance model for Malaysia, integrating lower monthly payments and reduced household debt. Key variables include house price appreciation rates, interest rates, initial guarantee fees and loan-to-value ratios. Inspired by the Help to Buy (HTB) scheme, the model aligns with proven global initiatives for enhanced affordability, balancing payment amounts, loan interest rates and acceptable price thresholds.

The study’s findings promise to address affordability disparities and reshape Malaysia’s housing finance landscape. The emphasis is on introducing a structured repayment plan that offers a sustainable path to homeownership, particularly for low-income families. Incorporating the future value adaptation concept, inspired by reverse mortgages and Islamic finance, enhances adaptability, ensuring long-term sustainability despite economic shifts.

The proposed model promotes widespread access to homeownership, offering practical solutions for policymakers to improve affordability, prompting adaptable risk management strategies for financial institutions and empowering potential homebuyers with increased flexibility.

The study introduces a transformative housing finance model for Malaysia, merging elements from reverse mortgages, Islamic finance and the HTB scheme, offering potential applicability to similar systems globally.