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This paper aims to empirically examine the main determinants of the compliance level of disclosure requirements for IAS 16, as well as factors that may explain the differences in the levels of compliance within companies.

The association between the level of compliance and various corporate characteristics was examined using Chi-square Automatic Interaction Detector (CHAID) analysis in financial disclosures for IAS 16. CHAID analysis was applied to the manufacturing companies listed in Borsa Istanbul for the years 2012 and 2013.

It was found that the most significant factor is the auditor reputation within different nodes such as size or free float rate. In most of the studies, correlation is used to determine the association between different factors, but this study is the first one that uses the CHAID analysis which offers an adjusted significance testing, and at the same time classification of the interaction between variables.

This paper provides insights into the primary factors of disclosure compliance that help to improve the structure of disclosures and the level of compliance in preparing future financial reports. The proposed improvements will also support further developments in financial reporting regulations regarding disclosures. The key limitation in this paper is that it concentrates on a specific standard and only covers two years. However, it provides suggestions for one of the most important standards that includes various disclosures.

In addition, this paper fills a gap in the literature about the compliance level of specific standards such as IAS 16 and the usage of CHAID analysis in such studies. The results were consistent with some previous studies regarding the relationship between compliance level, auditor reputation and size and it also highlight the effect of different disclosure items on compliance level.

The purpose of this paper is to investigate the day of the week (DoW) effect in stock markets of 19 countries with a predominantly Muslim population over the world.

The empirical research was conducted by using the descriptive statistical analysis and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) method in 19 stock markets for the past decade.

The findings in this paper present the evidence of the DoW effect in the majority of the stock markets analyzed. The findings were also consistent with the results of some previous studies regarding the DoW effect in various countries but some were found to be surprisingly different.

This study puts forward the view that investors may consider DoW diversities for their investment decisions regarding the countries with predominantly Muslim population. The authors conclude that additional factors affecting Islamic countries’ stock markets such as geographic proximity, trading days, market capitalization and ethnicity should be considered as well.

Researchers have shown an increased interest in calendar anomalies in stock exchanges of some individual Arab countries. This study contributes to the literature by examining Muslim country stock markets collectively.

This study explores the importance of application search engine (ASE) technology in the omni-channel strategy. For this purpose, this chapter firstly explains the concepts of the omni-channel and the search engines and the importance of them. Then, omni-channel in the framework of ASEs is discussed. Finally, recommendations for further researches are presented.

The motivation for this paper stems from the 2001 financial crisis which emerged in the banking industry and spread over the other industries, creating a domino effect. The purpose of this paper is to examine the market efficiency of Istanbul Stock Exchange (ISE) listed non-financial firms from 2000 through 2002.

A four-stage data envelope analysis (DEA) is developed to measure the performance of firms before and after the 2001 financial crisis. At each stage, production, profitability, marketability and overall efficiencies are measured. Further, Malmquist Productivity Index is applied to compare total factor productivity over time.

The findings show that firms are more efficient at the profitability stage than at other stages. However, the 2001 financial crisis eroded profitability efficiency. Overall, ISE-listed firms experienced diseconomies of scale so that many firms were not able to transform production into sales and therefore earnings efficiently, particularly during the crisis period.

The sample is limited to manufacturing companies. All financial firms are excluded from the sample.

This paper extends the three-stage market value efficiency process outlined in Zhu (2000) by adding production stage. It proposes four-stage DEA approach to measure production, profitability, marketability and overall efficiency of ISE-listed firms. To the best of authors’ knowledge, there has been no study using four-stage DEA approach for Turkish firms.

In recent years, with the development of technology, the number of contact points between companies and their customers has multiplied. From the company point of view, companies may reach their customers through multiple marketing channels. Moreover, business intelligence necessitates increasing data sources, strengthening the power of analysis tools, and developing knowledge to be used as a competitive advantage. On the other hand, today’s mostly digitized customers expect more than just commoditized products or services. Customer activation creates experiences that make them feel strong as agents perpetrating the structure (brand strategies) by taking an active role instead of being passive. In other terms, customers are in the main decision position to plan the structure. Thus, companies should design unique and memorable customer experiences through different channels in an integrated way, which is called omnichannel. Omnichannel customer experience management is possible by determining and coordinating customer touch points. In other terms, a melody should exist at these interaction points. This book aims to contribute to this advancement by first providing general reviews of the literature, then covering the pillars to design omnichannel customer experiences, and lastly providing technology-enhanced applications from several industries. This book aims also to provide fresh conceptual insights and thinking about the ways to design and develop omnichannel customer experiences.

Learning management systems (LMS) provide detailed information about the processes through event-logs. Process and related data-mining approaches can reveal valuable information from these files to help teachers and executives to monitor and manage their online learning processes. In this regard, the purpose of this paper is to present an overview of the current direction of the literature on educational data mining, and an application framework to analyze the educational data provided by the Moodle LMS.

The paper presents a framework to provide a decision support through the approaches existing in process and data-mining fields for analyzing the event-log data gathered from LMS platforms. In this framework, latent class analysis (LCA) and sequential pattern mining approaches were used to understand the general patterns; heuristic and fuzzy approaches were performed for process mining to obtain the workflows and statistics; finally, social-network analysis was conducted to discover the collaborations.

The analyses conducted in the study give clues for the process performance of the course during a semester by indicating exceptional situations, clarifying the activity flows, understanding the main process flow and revealing the students’ interactions. Findings also show that using the preliminary data analyses before process mining steps is also beneficial to understand the general pattern and expose the irregular ones.

The study highlights the benefits of analyzing event-log files of LMSs to improve the quality of online educational processes through a case study based on Moodle event-logs. The application framework covers preliminary analyses such as LCA before the use of process mining algorithms to reveal the exceptional situations.

This paper explored the new features of emerging stock markets, in order to point out the most associated indicators of increasing stock return volatility, which may lead to instability of emerging markets. The study covers a sample of five geographical areas of emerging economies, including Mexico, Korea, South Africa, Turkey, and Malaysia. It used the backward multiple‐regression technique to examine the relationship between monthly changes of stock price indices as dependent variable and the associated predicting local as well as international variables, which represent possible causes of increasing price volatility and initiating crises in emerging stock markets. The study covered monthly data for a period of forty‐eight months from January 1997 to December 2000. The study revealed that stock trading volume and currency exchange rate respectively represent the highest positive correlation to the emerging stock price changes; thus represent the most predicting variables of increasing price volatility. International stock price index, deposit interest rate, and bond trading volume were moderate predicting variables for emerging stock price volatility. While changes in inflation rate showed the least positive correlation to stock price volatility, thus represents the least predicting variable.

This paper aims to examine the stock market reactions of firms and industries in Malaysia to the government’s COVID-19 movement control order (MCO) announcement. As China is Malaysia’s leading trading partner, the authors also observe if the Chinese Government’s confirmation of human-to-human coronavirus transmission affects firms’ stock market reactions. In addition, this study examines whether the Malaysian Government’s ease of restrictions on economic activities affects firms’ stock market reactions. Finally, this study analyses the effect of COVID-19 number of confirmed cases on firms’ abnormal returns.

This study uses an event study methodology to determine the abnormal returns between day −30 to day 30 of the announcements. In addition, this study uses the regression estimation to determine whether the COVID-19 number of confirmed cases explain the abnormal returns.

This study finds that investors react negatively to the announcement of the MCO and confirmation of the human-to-human transmission of coronavirus over the event windows. However, the cumulative average abnormal returns (CAARs) started to recover when stimulus packages were introduced, and the lockdown measures were eased, allowing businesses to reopen. This study also finds that only firms in the health-care sector reported significant positive CAARs. Stock returns of the utilities and telecommunication firms showed no changes, while eight other sectors fell remarkably. The results also show that the COVID-19 number of confirmed cases adversely affects firms’ abnormal returns.

This study suggests that stock prices incorporate bad and good news surrounding the announcements of major international and local events related to the COVID-19 pandemic. Thus, investors should consider such factors in making investment decisions.

To the best of the authors’ knowledge, this paper is one of the early research works investigating the stock market reactions to the COVID-19 major announcements (MCO, human-to-human transmission and ease of restrictions on economic activities) using an event study methodology in an emerging market, namely, Malaysia. This study is timely in light of the recently increasing calls for researchers to analyse the potential economic impacts of COVID-19 on global capital markets, especially in emerging markets whose evidence is scarce.