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This paper aims to examine and compare the dividend ratios’ statistical and economic ability to predict the equity premium in the UK and US markets and two US sub-indices (S&P 500 Growth and S&P 500 Value).

In this paper, the authors use the linear regression models to examine the dividend ratios’ statistical ability to predict the equity premium. The in-sample and out-of-sample approaches, including Diebold and Mariano (1995) statistics, and Goyal and Welch’s (2003) graphical approach, are used. Also, the mean-variance analysis is used to test the economic significance.

The paper findings indicate that the dividend ratios have in-sample and out-of-sample predictive abilities in both UK and US markets and both US sub-indices. However, the results show that the dividend ratios have a less impressive predictive ability in the US market compared to the UK market and less in the US value index than the US growth index. This could indicate that there is no relation between the number of companies that distribute dividends in each index and the informativeness of dividends ratios. Furthermore, the tests show the dividend ratios’ predictive ability departure during particular periods and in some indices.

Results and implications of this research are exclusively applied to the US and UK markets. These results can also be applied with caution to other markets, taking into consideration the distinctive characteristics of these markets.

Results revealed in this paper imply that the investors in any of the indices may experience economic gain by adopting a dynamic trading strategy using the information content of the dividend ratios prediction models instead of the benchmark model, which is the prevailing simple moving average model.

This paper adds value through testing the prediction models’ economic significance in two well-developed markets, in addition to exploring the relationship between the number of companies distributing cash dividends and the dividends ratio prediction ability. Unlike most of the previous studies in which dividend ratios’ prediction ability is attributed to the number of companies that distribute dividends in the market, this paper denied this interpretation by studying two S&P 500 sub-indices. To the best of the authors’ knowledge, this is the first study to test the prediction models’ ability for these sub-indices.

The purpose of this paper is to re-examine the motivation to manage earnings in US mergers and acquisitions (M&As) by investigating whether the enactment of Sarbanes-Oxley act (SOX) has affected pre-merger earnings management.

The authors used a sample of over 700 completed M&As of US public firms during 1999-2008. Using quarterly reports, they tracked down earnings management during the four quarters preceding the deal and consequently drew inferences about the implications of SOX on interim reporting practices.

We report evidence that in the post-SOX era, non-cash acquirers begin pre-merger upwards earnings management in an earlier quarter than in the pre-SOX era. Further, our evidence indicates that in the quarters prior to the takeover, targets engage in more aggressive upwards earnings management in the post-SOX era.

Unlike what is anticipated regarding earnings management practices after SOX, the study reveals significant evidence of upward earnings management by firms engaging in M&A in post-SOX era.

The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains (SC) and to develop future research directions from thereof.

Using a systematic literature review methodology, this study analyzes the publications available on Web of Science, Scopus and Google Scholar that linked both AI and supply chain from one side and ML and supply chain from another side. A total of 388 research studies have been identified through the before said three database searches which are further screened, sorted and finalized with 50 studies. The research thoroughly reviews and analyzes the final lists of 50 studies that were found relevant and significant to the theme of AI and ML in supply chain management (SCM).

AI and ML applications are still at the infant stage and the opportunity for them to elevate supply chain performance is very promising. Some researchers developed AI and ML-related models which were tested and proved to be effective in optimizing SC, and therefore, the application of AI and ML in supply chain networks creates competitive advantages for firms. Other researchers claim that AI and ML are both currently adding value while many other researchers believe that they are still not fully exploited and their tools and techniques can leverage the supply chain’s total value. The research found that adoption of AI and ML have the ability to reduce the bullwhip effect, and therefore, further supports the performance of supply chain efficiency and responsiveness.

This research was limited in terms of scope as it covered AI and ML applications in the supply chain while there are other dimensions that could be investigated such as big data and robotics but it was found too lengthy to include these additional dimensions, and therefore, left for future research studies that other researchers could explore and pursue.

This study opens the door wide for other researchers to explore how AI and ML can be adopted in SCM and what are the models that are already tested and proven to be viable. In addition, the paper also identified a group of research studies that confirmed the unexploited avenues of AI and ML which could be of high interest to other researchers to explore.

Although few earlier research studies touch based on the AI applications within manufacturing and transportation, this study is different and makes a unique contribution by offering a holistic view on the AI and ML implications within SC as a whole. The research carefully reviews a number of highly cited papers classifying them into three main themes and recommends future direction.

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

This study aims to identify and analyze factors affecting the business-to-business (B2B) relationship between Sri Lankan telecommunication operators and vendors. The authors conduct a survey and develop models to explain relationship strength and satisfaction. The authors find that telecommunication operators and vendors value trust, commitment, adaptation and communication. Operator satisfaction varies by perception of product quality, service support, delivery performance, supplier know-how and value for money. The vendor’s relationship strength is impacted by trust and commitment; vendor satisfaction is affected by economic factors and referencing. The authors suggest formulating management strategies using these results to strengthen business relationships.

The authors develop two conceptual models to analyze the supplier and customer perspectives. This study’s drafted models were drawn from established models and were presented to experts in the industry, both telecommunication operators and vendors. Models were modified based on experts’ feedback, and hypotheses were developed from the conceptual models, developed separately for the two perspectives. Data collection was done via questionnaires; 150 questionnaires were sent via email to identified telecommunication operators and 100 questionnaires were sent via email to identified telecommunication vendors, with follow-up emails and telephone calls to improve response rates.

This study’s findings show that employees in the telecommunication industry recognize the importance of B2B relationships. Employees of both telecommunication operators and vendors agree that stronger relationships are advantageous. The correlation and regression analysis results identify factors that affect the B2B relationship. The following factors impact the strength of B2B relationships irrespective of view point: trust, commitment and satisfaction. The following factors were found to significantly affect the strength of B2B relationships between telecommunication operators and vendors from the operator perspective: adaptation and communication.

To enhance relationship strength, the management of operator organizations should take action to improve trust, commitment and satisfaction. Demonstrating honesty and integrity when dealing with vendors and exhibiting concern for the other party’s interests can help establish trust or enhance trust in existing relationships. Displaying commitment toward the vendor will also facilitate stronger relationships. Reasonable profits for both parties and sizeable business volume will also help satisfy vendors, increasing relationship strength. Positive referencing of the vendor in industrial and public forums will improve vendor satisfaction, enhancing relationship strength. Reputational capital can be built and maintained for both operators and vendors by keeping promises and defending the other party to outsiders. For managers of telecommunications operators and vendors in other emerging markets, this study’s results are important and can inform internal business practices to support trust, commitment and satisfaction.

This study contributes to the existing literature in two ways, a focus on the telecommunication industry and a previously unexplored emerging market, Sri Lanka. In addition, this study includes an analysis of the relationship from both the operator and vendor perspectives.