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Micro, small and medium-sized enterprises (MSMEs) are the backbone of economic development for every economy. They contribute to local economic development through household wealth creation, employment generation and poverty reduction. Despite this pivotal role, MSMEs lack access to finance, and scholarship on the enabling role of financial inclusion on micro, small and medium-sized enterprises' performance is scant. The authors contribute to closing the knowledge gap by examining the enabling effect of financial inclusion on MSMEs using the FinScope MSME 2017 survey for the Kingdom of Eswatini. This paper aims to discuss the aforementioned objective.

The study used the re-centered influence function regression framework to estimate unconditional quantile regressions and the generalized ordered logit model to analyze the data.

The findings from the unconditional quantile regression revealed that small changes in access to bank accounts, saving for business, formal saving, stokvel and informal saving at the 50th and 75th percentiles have a positive and statistically significant effect on microenterprises' annual turnover profit. Conversely, small changes in formal insurance have a mixed effect on annual turnover profit. At the 10th and 25th percentiles, a small increment in insurance reduces annual turnover profit but increases microenterprise annual turnover profit at the 75th percentile. Meanwhile, the evidence from the generalized ordered logit model showed that financial inclusion reduces the likelihood of microenterprises being classified as least developed and increased the chances of microenterprises falling into emerging and developed business categories.

This study makes use of a cross-sectional survey dataset, as a result, it does not infer causal relationships over the long term, but rather an association between the independent and dependent variables.

Overall, formal and informal financial inclusion enhances the annual turnover profit for microenterprises, particularly at the 50th and 75th percentiles in the Kingdom of Eswatini. The authors recommend a specialized institution such as a micro, small and medium-sized partial credit guarantee scheme to improve the quality and affordability of credit for microenterprises, and a mix of financial and non-financial supports depending on the development stage to boost a sustainable microenterprises' sector.

The study uses two advanced cross-sectional techniques, the recentered influence function framework and the generalized ordered logit model to analyze the data. The paper is original and contributes to the discussion of the role of financial inclusion in enabling microenterprises' success in Africa, using the FinScope 2017 survey of microenterprises in Eswatini as a case study.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2020-0689.

The purpose of this paper is to examine the effect of economic policy uncertainty (EPU) of China on investment opportunities in five ASEAN economies.

This paper employs advanced empirical approaches, such as Multivariate DCC-GARCH and Continuous Wavelet Transform (CWT) to test the research objective. The period of analysis involved monthly data from 2003 until 2019.

This paper provides evidence where the Malaysian stock market to be the least exposed to risks emanating from Chinese EPU, followed by Singapore, the Philippines, Thailand and Indonesia. Results for investment opportunities based on time horizon suggest, for a short-term holding period, investors are better off investing in Singapore and Indonesia, while, for medium-term holding periods, all ASEAN markets appear lucrative except for the Philippines.

From a managerial perspective, the outcome or findings of this study are expected to aid the retail and institutional investors in designing better strategies on diversifying a stock portfolio with different holding periods.

Theoretically, the findings of this study contribute fresh insights into an emerging strand of literature focusing on the transmission of regional policy. Methodologically as well, this study is a novel venture to the best of authors' knowledge.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

Ethics is fundamentally a science of social and collective responsibility. Ethics concerns human behavior as responsible or accountable. Because of the nature of social interaction, certain members of the society will bear greater authority, and hence, greater individual and social responsibility than others. In our world, personal responsibility and social responsibility are hardly separable. Personal responsibility becomes responsibility for the world because the person and the world are inseparable. In this chapter, we use the term responsibility from a legal, ethical, moral, and spiritual (LEMS) standpoint as some promise, commitment, obligation, sanctioned by self, morals, law, or society, to do good, and if harm results, to repair harm done on another. Hence, responsibility from a moral perspective is trustworthiness and dependability of the agent in some enterprise. Its inverse is exoneration – the extent to which one is excused from commitment and repairing the harm done to others by one’s actions. We apply the theories and constructs of executive responsibility to two contemporary cases: (1) India’s Super Rich in 2014 and (2) the Fall and Rise of Starbucks. After exploring the basic notion of responsibility, we present a discussion on the nature and obligation of corporate responsibility into three parts: Part I: Classical Understanding and Discussion on Corporate Responsibility; Part II: Contemporary Understanding and Discussion on Corporate Responsibility, and Part III: A synthesis of classical and contemporary views of responsibility and their applications to corporate executive responsibility.

This paper investigates the change talk (CT) strategies of the motivational interviewing (MI) technique and their relevance in achieving change goals within communities of practice (CoP), focusing on addressing real-world problems in today's complex world.

We employ a literature review and conceptual analysis to study the interactions and potential areas of complement between CT, MI and CoP theories.

This paper combines CT, MI and CoP theories to develop an integrated model called Facilitative Change Talk Leadership (FCTL).

This paper provides an innovative model (FCTL) to inform leadership educators about facilitating communities of practice. We provide a hypothetical case study to suggest how FCTL might foster collaborative inquiry and resilience amidst complex challenges. This case study illustrates a practical pathway for leadership educators and community practitioners to use this model in their own contexts.

To identify the Transcendental Essence of Humanity, the purpose of this paper is to describe in brief what kind of research became possible when the theory of, e.g. autopoiesis, Husserl’s Transcendental Consciousness and the theory of Rhodes and Thame came together to form a “transcendental” interview methodology.

Critical conceptual implications are drawn to form a new research method to explore a de-subjectified inner domain and to search for a possible common essence of humanity.

A Transcendental Emotional Reference was found practically alien to contemporary perspectives. Still, the reference governs the emotional structure of human experience. This different perspective answers basic questions of morality, organization theory and leadership.

The findings of the new research open a new and transparent perspective answering Grey’s question: “What is it to be human?” (Grey, p. 47, 2014.) A perspective shedding new light on the humanities. A research limitation is the number of respondents. Still, being transcendental the findings are theoretically valid for all.

The paper is based on a unique research enabling 32+ (ongoing research) respondents to explore their own and universally shared Transcendental domain.

The predominant neoliberal structure of capitalism and tourism as the fuel of capitalism exposes growing problems of injustice, unfairness and inequality. Places and communities around the world are currently expressing the need for radical changes in placemaking to be able to think, plan and act differently. This theoretical contribution adopts a humanistic management (HM) perspective of placemaking to promote places where people enjoy living, working, interacting and having meaningful experiences. Tourist destinations are relevant places to discuss the application of HM principles in practice and promote humanistic destinations and the humanisation of placemaking. This chapter concludes by arguing for an interface with eco-centric and posthumanist transformative approaches to promote holistic value-based placemaking and regeneration of places.

The study considers time-varying risk premium in investigating the capability of technical analysis (TA) to predict and outperform a buy–hold strategy in Bitcoin exchange rate returns.

The study tests the technical trading rule of fixed moving average (FMA) on daily actual and equilibrium returns of Bitcoin exchange rates. The equilibrium returns are computed using dynamic CAPM in conjunction with a VAR-MGARCH (1, 1) system. The empirical evaluation of the study uses a case study of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR) for the period 19 June 2010 to 30 October 2020.

The findings are consistent with related studies in conventional foreign exchange markets that find TA to be profitable, especially in emerging markets. Nevertheless, the consideration of risk premium has the effect of reducing the abnormal returns. Also, further robust tests reveal that Bitcoin returns possess a momentum effect which prompts further study in efficient market hypothesis research.

The empirical findings of this study should benefit portfolio managers and active investors on the strength of TA to predict returns in a speculative market like the Bitcoin exchange rate market.

The study takes cognisance that cryptocurrency trading is speculative in nature which renders it a good candidate for TA methods. While there are studies that have explored the value of TA in Bitcoin exchange rates, these studies fail to incorporate the effects of time-varying risk premiums, the strength and focus of the current paper.

Despite the relevance of innovation in entrepreneurship literature, empirical research on the innovation-performance relationship in start-ups is underdeveloped and shows controversial results. To bridge this gap, the aim of this paper is to investigate the role of innovativeness on new venture performance in the early stage of the life cycle.

Regression modelling and propensity score matching are used to reveal systematic differences in growth between innovative start-ups (ISUPs) and non-innovative start-ups. We use an ad hoc dataset obtained through merging the financial database AIDA with data from administrative sources (Italian Chambers of Commerce and the Italian Ministry for Economic Development).

The results show that differences in growth can be explained by the different levels of innovativeness in new ventures. Moreover, unlike in prior studies, the innovation inputs matter more than innovation outputs. Indeed, the results support the idea that innovation policies can contribute to maximising the potential of start-ups.

The findings provide suggestions for policy makers and entrepreneurs to help firms configure ex ante appropriate actions to support the growth of new ventures in the start-up stage.

This study is the first to use the new objective measure of start-up innovation, available from the Italian LD 221 register. Second, different types of innovation are investigated as antecedents of firm growth. Third, we employ propensity score matching, which favours revealing systematic differences in growth between ISUPs and non-innovative start-ups. Fourth, the results of our study are the first to offer evidence on the effectiveness of the new Italian sustaining ISUPs policy.