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This article aims to explain the role of philosophical anchors and research paradigms in business research, and how they can be extrapolated in the transformative era of automation, digitalization, hyperconnectivity, obligations, globalization and sustainability (ADHOGS) in the midst of disruption, volatility, uncertainty, complexity and ambiguity (DVUCA).

This article entails a general review based on the 3Es of exposure, expertise and experience, delving into the ontological, epistemological, methodological, axiological and rhetorical aspects of the major research paradigms—i.e. positivism, post-positivism, constructivism, interpretivism and pragmatism—and their interplay with the emergent trends shaping business research.

This article underscores the multifaceted nature of business research in the modern day, with an increasing need for blending, or shifting between, research paradigms to address the complex issues arising from automation, digitalization, hyperconnectivity, obligations, globalization and sustainability (ADHOGS). This article also highlights the nuanced interplay between research paradigms and theoretical perspectives, demonstrating the rich, diverse potential of business research inquiries.

While this article provides a broad overview of the interplay between research paradigms and emerging trends, future research could explore each of these interplays in greater detail, conducting empirical studies or utilizing specific case studies.

Researchers and practitioners should be open to adopting, combining or switching between different paradigms according to the demands of their research questions, context and trends shaping the business landscape, thereby underscoring the need for methodological flexibility and reflexivity in business research.

The shift toward embracing digital transformations and integrating sustainability in business research holds significant implications, driving socially responsible and sustainable business practices at the micro-level, and by extension, industrial revolution and sustainable development at the macro-level.

This article offers a holistic and contextualized view of the philosophy of science and research paradigms for business research, bridging the gap between philosophical foundations and contemporary research trends.

Using textual analysis the authors study the relationship between social media sentiments and stock markets during the COVID-19 pandemic.

The study analysis is based on a sample of 1,616,007 tweets over the period January to June 2021 for seven countries. The authors process the tweets via the VADER analyzer thereby producing both positive and negative sentiment measures.

Particularly, the authors prove that higher positivism is associated with a short-term increase in stock prices. On the other side, negativism relates inversely to stock prices with long-term impact, in the case of English-spoken countries. Notably, the study results remain robust to the inclusion of various control variables, including virtual fear and Google vaccine indexes. Finally, the authors prove that positivism is associated with higher returns and lower volatility in the short-run, while negativism is linked with lower returns in the short run.

The study analysis also has significant policy implications for researchers, investors and policymakers. First, researchers can employ our measures to quantify market sentiments and expand their research arsenal to incorporate social media trends, thus providing better explanatory power. Second, during times of severe uncertainty such as in a pandemic period, investors could beneficially take into account our textual measures and empirical results when using asset pricing models or constructing their portfolios. Third, the finding that the stock market is heavily governed by sentimental behaviors, especially during crisis periods, implies that policymakers including central banks, governments and capital market commissions must consider these sentiments before exerting their policies. In this regard, governments can effectively develop policy tools and approaches to manage recovery from the pandemic, which translates to greater long-term economic resilience. Moreover, central banks should accordingly adjust their monetary policy measures in order to stabilize financial markets, and by extension, to stop the pandemic from turning into a renewed financial crisis. For example, asset purchase program is considered the main instrument of this kind of intervention.

The authors confirm that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. The paper should be of interest to readers in the areas of finance.

This study demonstrates how individual paradigms implicate the questions asked, methods used and results drawn in association with a common object of study in human information behavior (HIB) research – the relationship between uncertainty and decision-making.

The author uses textual case studies to examine uncertainty and decision-making through the framework of four paradigms used in HIB research: positivism, cognitivism, collectivism and constructionism and suggests deconstructionism as a paradigm which raises new questions around this topic.

Positivistic approaches to uncertainty are often systems oriented; cognitive approaches are often user-oriented; collectivist approaches are intersubjective; and constructionist approaches blend a subjective and intersubjective research orientation. Deconstructionism raises new questions around ethics and responsibility in relation to decision-making, and the author therefore situates it as a new paradigmatic approach for this topic in HIB research.

Despite the presence of research aimed at recognizing and defining paradigms in HIB research, a comparative micro-examination of how individual paradigms implicate a specific research topic has yet to be conducted. Each paradigm uniquely shapes the ways in which uncertainty and decision-making are characterized, but the four central ones examined here have thus far left out questions of ethics and responsibility as being core elements of decision-making as tied to uncertainty. Therefore, this paper introduces deconstructionism as a paradigm new to HIB uncertainty research, arguing that it provides an important and novel complication of existent research questions and approaches.

This article examines the effect of China's soft power projection through the Chinese media in Africa on the African audiences' perception of China's national image through a case study of the residents of Nairobi. It adopted Joseph Nye's soft power model and sought to address three fundamental questions : What is the extent and objective of China's media diplomacy in Africa? How has China's media “offensive” in Africa impacted African audiences' imagery of China? What are the implications, if any, of China's media diplomacy on the Kenyan public view of China?

This article used a mixed-methods research design, which deployed elements of positivism and interpretivism. It used a deductive approach and deployed the survey strategy, which entailed the collection of data from Nairobi city residents. The secondary data were collected from relevant academic literature sources. The primary data were analyzed empirically using the Statistical Package for the Social Sciences (SPSS), while the secondary data were analyzed using discourse analysis.

China's soft power projection through the Chinese media in Africa is aimed at addressing the “misinformation” about China. China seeks its share of representation among the African public to correct negative perceptions of China. Kenyans had a generally positive attitude toward China. South Africa and Angola have “Fairly” positive perceptions of the Chinese media. However, this study did not reveal whether the perception was due to the influence of Chinese media. These findings implied that the African public's positive imagery of China cannot be fully attributed to Chinese media's influence.

This study is groundbreaking in that it is one of the few studies that have focused on China's public diplomacy in Africa and assessed the impact of Chinese media on the African public’s perception of China.

The purpose of this study is to introduce the concept of workplace camaraderie and to investigate the mechanism through which workplace camaraderie influences incivility at the workplace. The study is explained by taking the sequential mediation of personal biases leading to cronyism and favoritism. Social identity theory is used as the underpinning theory to explain the framework adopted.

Positivism research philosophy followed by the deductive approach is followed to meet the objectives of the current study. In total, 171 employees working in public sector organizations were taken as the respondents to the study. A purposive sampling technique was used to collect the data through self-administrated questionnaires. Path model is used through Mplus to generate the results and test hypotheses.

The results suggested that workplace camaraderie significantly affects incivility at a workplace with the sequential mediation of personal biases leading to cronyism and favoritism.

Although several researchers have studied the link between camaraderie and other employees’ related attitudinal and behavioral outcomes, few have explored the roles of personal biases, cronyism and favoritism in the relationship to incivility. This study thus posits a novel sequential mediation mechanism, based on the social identity theory, through which camaraderie is translated into civil behavior. Moreover, this study adds value by investigating this model in the public sector, where camaraderie can come up with important consequences.

Business simulation games (BSGs) are gaining popularity in higher education as tools for entrepreneurial education. However, there are challenges in using BSGs for large classes of students in historically disadvantaged institutions (HDIs). The challenges include limited resources and digital literacy skills.

The living standards measure (LSM) indicates socioeconomic status by measuring the degree of urbanisation, access to services and possession of assets, such as mobile phones. This research investigated the impact of students' living standards on their intention to use BSGs, as there is limited research.

This study used positivism and added LSM to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Quantitative data from 224 third-year information system students at an HDI were collected through an online survey. Structured Equation Modelling was used to analyse the data.

Results indicated that performance expectancy and social influence significantly influenced the intention to use BSGs. However, the relationship between effort expectancy and behavioural intention facilitating conditions was not significant. The introduction of LSM as a moderator for the relationships between all variables and behavioural intention also proved to be insignificant.

The result was unexpected, as we posited that a lower LSM would affect students' intention to use BSGs. This was not proven in this study and could be related to students accessing campus resources. However, during times when access to campus is restricted, such as protest action, the result may change for students with lower LSM.

Common literature review methods such as systematic review and narrative review are poorly suited to the investigation of complex management phenomena. Systematic reviews are highly driven by protocol and procedure, and are oft-criticized as reductive and poorly equipped to examine the interaction between phenomena and context, nonlinear processes, and empirical outcomes that are less predictable. Narrative reviews, on the other hand, are pluralistic and iterative and thus better suited to descriptions of the complex and unpredictable; however, they tend to lack methodological transparency, trustworthiness, and pragmatism in application. The “realist synthesis” approach to literature review can be seen as the middle-ground between these two common methods, offering both methodological rigor alongside flexibility and nuance. Realist synthesis takes an explanatory frame, with a focus on unearthing the theorized causal mechanisms at play beneath a phenomenon of interest.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

This paper presents the findings from the assessment of the determinants of cloud computing (CC) deployment by construction organisations. Using the technology-organisation-environment (TOE) framework, the study strives to improve construction organisations' project delivery and digital transformation by adopting beneficial technologies like CC.

This study adopted a post-positivism philosophical stance using a deductive approach with a questionnaire administered to construction organisations in South Africa. The data gathered were analysed using descriptive and inferential statistics. Also, the fusion of structural equation modelling (SEM) and machine learning (ML) regression models helped to gain a robust understanding of the key determinants of using CC.

The study found that the use of CC by construction organisations in South Africa is still slow. SEM indicated that this slow usage is influenced by six technology and environmental factors, namely (1) cost-effectiveness, (2) availability, (3) compatibility, (4) client demand, (5) competitors' pressure and (6) trust in cloud service providers. ML models developed affirmed that these variables have high predictive power. However, sensitivity analysis revealed that the availability of CC and CC's ancillary technologies and the pressure from competitors are the most important predictors of CC usage in construction organisations.

The paper offers a theoretical backdrop for future works on CC in construction, particularly in developing countries where such a study has not been explored.