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This study attempts to assess the role that institutional quality (IQ) plays in influencing inflows and outflows of Foreign Direct Investment (FDI) for BRICS nations as burgeoning FDI is flowing into and out of these countries. Moreover, this paper explores the impact of individual governance indicators separately on the FDI flows.

The study analyses this nexus for these emerging economies for the period 1996–2019 using autoregressive distributed lag technique.

The study indicates a significant and positive coefficient for IQ in India and South Africa, suggesting that improving IQ would enhance the IFDI. However, for outward FDI (OFDI)–IQ linkage, the results show a negatively significant impact of IQ on OFDI for Brazil and Russia. Additionally, the authors observe control of corruption as a significant institutional component for attracting inward FDI for Brazil, India and South Africa, whereas it is an insignificant factor for Russia and China. Further, the authors notably find that upgrading the governance indicators will decrease the level of OFDI for Brazil, Russia, China and South Africa. On the contrary, findings suggest that improving the IQ will foster the OFDI for India.

This study uses time-series analysis instead of cross-country analysis (used extensively in literature), avoiding heterogeneity. Further, this study explores the IFDI–IQ link for BRICS nations, which are captivating a significant chunk of IFDI, and still not given much attention in the extant literature. Moreover, the authors identify the impact of IQ on the OFDI, neglected by the existing studies.

Substantial pressure from civil society and investors has forced governments around the world to take climate neutrality initiatives. Several countries have pledged their nationally determined contributions towards net-zero. However, there exist various obstacles to achieving the same and the agriculture sector is one of them. Thus, this study identifies and models the critical barriers to achieving climate neutrality in the agriculture food supply chain (AFSC).

Sixteen barriers are identified through a literature survey and are validated by the questionnaire survey. Furthermore, the interactions amongst the barriers are estimated through the application of the “weighted influence non-linear gauge system (WINGS)” method which considers the both intensity of influence and the strength of the barrier. To mitigate these barriers, a framework based on green, resilient and inclusive development (GRID) is proposed.

The obtained results reveal that lack of collaboration amongst AFSC stakeholders, lack of information and education awareness, and lack of technical expertise obtained a higher rank (amongst the top five) in three indicators of the WINGS method and thus are the most significant barriers.

This paper is the first attempt in modelling the climate neutrality barriers for the Indian AFSC. Additionally, the mitigating strategies are prepared using the GRID framework.

Today’s in-demand skills may not be needed tomorrow. As companies are adopting a new group of technologies, they are in huge need of information technology (IT) professionals who can fill various IT positions with a mixture of technical and problem-solving skills. This study aims to adopt a sematic analysis approach to explore how the US Information Systems (IS) programs meet the challenges of emerging IT topics.

This study considers the application of a hybrid semantic analysis approach to the analysis of IS higher education programs in the USA. It proposes a semantic analysis framework and a semantic analysis algorithm to analyze and evaluate the context of the IS programs. To be more specific, the study uses digital transformation as a case study to examine the readiness of the IS programs in the USA to meet the challenges of digital transformation. First, this study developed a knowledge pool of 15 principles and 98 keywords from an extensive literature review on digital transformation. Second, this study collects 4,093 IS courses from 315 IS programs in the USA and 493,216 scientific publication records from the Web of Science Core Collection.

Using the knowledge pool and two collected data sets, the semantic analysis algorithm was implemented to compute a semantic similarity score (DxScore) between an IS course’s context and digital transformation. To present the credibility of the research results of this paper, the state ranking using the similarity scores and the state employment ranking were compared. The research results can be used by IS educators in the future in the process of updating the IS curricula. Regarding IT professionals in the industry, the results can provide insights into the training of their current/future employees.

This study explores the status of the IS programs in the USA by proposing a semantic analysis framework, using digital transformation as a case study to illustrate the application of the proposed semantic analysis framework, and developing a knowledge pool, a corpus and a course information collection.

Investors who can transfer their savings to investments in a well-regulated market benefit not only themselves but also economic development. Hence, it is crucial for fund owners to evaluate their stock market investment decisions. The goal of the study is to understand which model determines the asset returns most efficiently. In this regard, the validity of single and multi-index asset pricing models (capital asset pricing model-CAPM and Fama–French models) has been examined in the Turkish Stock Exchange for 2009–2020, with the quantile regression (QR) approach.

On 18 portfolios comprised of quoted stocks in the Istanbul Stock Exchange 100 (ISE-100/BIST-100), we test the CAPM, the Fama and French three factor model (FF3) and the Fama and French five factor model (FF5). Empirical analyses have been carried out via QR approach regressing the portfolios' average weekly excess returns on risk premium/market factor (Rm-Rf), firm size, book value/market value (B/M), profitability and investments factors. QR estimation has been employed since QR is more effective and provides a better definition of the distribution’s tails.

Our empirical findings have revealed that the average excess weekly returns can be explained more strongly via CAPM. Moreover, Fama and French models are expected to give more reliable result with more data, whereas the market premium would give robust results for the Turkish Capital Market.

Individuals investing in financial assets must find the price model that best fits the market. The return can be approximated in the most appropriate manner using the right variables.

The study differs from other research by comparing the asset pricing models via examining the assets' weekly returns with QR in the Istanbul Stock Exchange 100 (ISE-100).