Search results

This paper aims to investigate the effects of audit quality on the cost of capital in Ghana.

Non-financial firms listed on the Ghana Stock Exchange (GSE) as well as non-listed firms from the database of Ghana Club 100 were included in the sample. Series are yearly, covering a sample of 40 firms during the six-year period, 2008-2013. The study employed the positivist research paradigm to establish the relationship between audit quality and the cost of capital.

There is evidence to suggest that the cost of debt and the overall cost of capital of firms in Ghana can be explained by the quality of the external auditors. The results also show that the large size of the board is associated with low cost of debt.

The fact that the choice of quality measure is based on firm size only and other measurements of audit quality could not be measured. Future research may examine how other approaches to measuring audit quality affect cost of capital.

The results significant for those charged with assurance and regulation, as well as lenders and managers of companies.

The authors investigate how external auditing quality affects the cost of capital of firms operating in Ghana.

This study aims to investigate the effects of international financial reporting standards (IFRS) adoption and firm size on auditors’ fees determination in the Ghanaian financial industry.

The authors use the annual report of 52 listed and non-listed firms spanning from 2003 to 2014. Guided by the hypotheses, the authors conditioned audit fees on IFRS adoption and firm size and execute robust fixed effects panel regression.

The results show that IFRS adoption has a positive coefficient with audit fees suggesting that the adoption of IFRS, indeed, increases the audit fees paid by banks and insurance firms, as well as the industry as a whole. The results are consistent with the idea that IFRS adoption increases auditor efforts with respect to time and complex nature of some aspect of the standards. Again, as expected, the coefficient of size is positively and significantly related to audit fees. This indicates that the size of the auditee plays a vital role in determining audit fees.

The study is limited by industry (i.e. the financial services industry) and geography (i.e. Ghana). The authors propose further research that will widely consider other sectors and countries to improve the current scanty literature in this area. Besides, theoretically, the study is limited to the lending credibility theory and feels compelled to reiterate the importance of considering alternative theoretical perspective(s) in future research.

This study is significant to practitioners as it demonstrates the importance of the determinants of the auditors’ fees. It helps auditors to apply the relevant charging formula when determining audit fees, while it helps managers to improve upon the quality of reporting to control audit bill and forecasting their audit expenditure.

The results of the study extend the literature on the cost side of IFRS adoption by investigating the financial services industry and non-listed firms in a new context, i.e. a developing country where this research is uncharted. The existing studies based their analysis on either cross-section or pooled analysis and shorter post-adoption period (Cameran and Perotti, 2014). However, using an extended post-adoption period data, the authors base the study on analytical panel model, which directly examine the cost side of IFRS adoption with size as joint key explanatory variables with emphasis on financial institutions and external auditors.

The purpose of this study is to further increase the understanding of disclosure practices and the interrelationship between corporate governance (CG) and corporate disclosure (CD) of firms on the Ghana Stock Exchange (GSE).

The study follows the trinary procedure of Aksu and Kosedag and uses Standard & Poor's T&D items in the construction disclosure index. Audit committee (AC) characteristics are the governance attributes. The study used a random effect panel regression analysis to establish the relationship between CD and CG of 20 listed companies covering a period from 2003‐2007.

The results indicate that although there has been improvement of disclosure practices over the years, the level of disclosure in Ghana is moderate/fair. The study also documents a significant positive relationship between the presence of accounting/finance expert(s) on the ACs and CD practices.

In spite of numerous researches on companies on the GSE, this paper is the first in the country that considers the impact of CG characteristics on disclosure practices.

This study aims to determine the influence of corporate governance characteristics on carbon emission disclosure in an emerging economy.

The study is based on S&P BSE 500 Indian firms for the period of 6 years from 2016–2017 to 2021–2022. The panel data regression models are used to gauge the association between corporate governance and carbon emission disclosure.

The empirical findings of the study support the positive and significant association between board activity intensity, environment committee and carbon emission disclosure. This evinced that the board activity intensity and presence of the environment committee have a critical role in carbon emission disclosure. On the contrary, findings reveal a significant and negative relationship between board size and carbon emission disclosure.

The present study provides treasured insights to regulators, policymakers, investors and corporate managers, as the study corroborates that various corporate governance characteristics exert significant influence on carbon emission disclosure.

The current research work provides novel insights into corporate governance and climate change literature that good corporate governance significantly boosts the carbon emission disclosure of firms. Previous studies examining the impact of corporate governance on carbon emission disclosure ignored emerging economies. Thus, the current work explores the role of governance mechanisms on carbon emission disclosure in an emerging context. Further, to the best of the author’s knowledge, the current study is the first of its kind to investigate the role of corporate governance on carbon emission disclosure in the Indian context.

The current study aims to advance the research on the extra-industry network, innovativeness and performance of small and medium enterprises (SMEs) by examining the mediation role of proactiveness. The study also aims to examine the mediating role innovativeness between extra-industry network and performance.

This study used the proportionate stratified random sampling method to select the study sample and the questionnaire survey approach to 580 SMEs. A total of 150 completed questionnaires were returned. Partial least squares structural equation modeling was administered to analyze data via Smart PLS 3.0 software.

The results reflect that proactiveness is mediated by the relationship between the extra-industry network and the performance of Iranian SMEs. In addition, the results illustrated that proactiveness is mediated by the relationship between innovativeness and the performance. The findings also address the limitation of previous studies on Iranian SMEs through the independent examination of the mediating role of innovativeness between firm extra-industry network and performance.

This article is one of few attempts that have addressed the significance of proactiveness as the key mechanism to transform the advantages of extra-industry network and innovativeness relationships to enhance performance.

The purpose of this paper is to explore the approaches used by researchers in examining the influences of external factors towards voluntary disclosure in emerging countries.

The data collected in this study were collected through a review of empirical literature based on 35 articles published between 1998 and 2013. The sample articles on the link between external factors and the level of voluntary disclosure were located by searching keywords in the most relevant social science research databases such as Business Source Premier, Emerald full text, JSTOR, Science Direct, Scopus, and Social Science Research Network.

The result reveals that research in voluntary disclosure practices by companies in emerging countries remains low. The majority of studies employed content analysis to examine the extent of voluntary disclosure practices. Results from studies show that greater regulatory enforcement in the region and increase in stakeholders’ comprehension about their rights and choices with regards to business activities can influence the majority of the companies to provide voluntary disclosure. The literature revealed that social responsibility and environmental information are the popular categories of voluntary disclosure while risk and human capital/intellectual capital are the least popular categories.

The paper is limited to a review of 35 articles.

The study provides avenues for policy makers and regulators to carry out reforms on voluntary disclosure practices.

The findings may provide insights to capital market regulators when conducting effective regulation and supervision of information transparency in listed companies.

Since limited studies exist that examine voluntary disclosure in emerging countries, little is known about the implications of external factors such as a country’s policy, regulations, stakeholders, and business environment on voluntary disclosure practices. This paper contributes to filling this gap by a review of articles of empirical research on voluntary disclosure in emerging countries.

This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution; Second, this paper elaborates on those estimates’ usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons.

This study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models.

The results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naïve I model).

A discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors’ experience and tourism stakeholders’ decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures.

High-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week.

This research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors’ experience and public and private decision-making.

The aim of this paper is to examine the effects of the disconfirmation of expectations of crowding and mall accessibility, on stress and two marketing outcomes, satisfaction and promoter scoring.

Data were obtained through two face-to-face surveys from mall shoppers that answered them at two different moments of their shopping experience, before entering the mall and before leaving it. Results are obtained from 230 customers that answered the two questionnaires.

The findings suggest that stress indirectly influences customer promoter scoring through satisfaction, while disconfirmation of expectations influences it directly and indirectly.

These results also suggest that stress and disconfirmation of expectations about crowding and accessibility are important in determining promoter scoring. To reduce stress and increase satisfaction and promoter scoring, managers should focus on exceeding customers' expectations about mall accessibility and on ensuring that customers experience a lower level of crowding than they expected.

The article examines Net Promoter Scoring, an outcome that has attracted managers' attention but little is known about its antecedents. The paper provides evidence of the effect of disconfirmation of expectations and negative emotions on promoter scoring.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.