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The purpose of this paper is to comparatively examine the cost and the overlooked revenue efficiency of Islamic and commercial banks in the aftermath of the crisis, operating in nine MENA-based countries during the 2010-2017 financial period, where the established empirical work is relatively limited. The authors also update the research where they use recent data sets and they provide for a targeted, structured literature review pre- and post-crisis in the Gulf region.

The authors examine cost and revenue efficiency of 25 major Islamic banks (IBs) and 25 major conventional banks (CBs). They conduct tests on the determinants of such variables. In the first stage of the analysis, they measure efficiency by using the data envelopment analysis (DEA) technique. The analysis performs regressions where these also reveal that the bank efficiency index is influenced by various bank type-specific attributes. It also seems that tighter restrictions on bank activities are negatively associated with bank efficiency. Second stage analysis, which accounts for banking environment and bank-level characteristics, confirms these results.

Conventional banks are both more cost and revenue efficient than Islamic banks over the period under examination. The analysis also reveals that the bank efficiency index is influenced by bank-type attributes. Greater presence of fixed capital resources has positive effects on growth in both Islamic and conventional banking. The major constraints impeding Islamic banking growth include labour costs. The authors examine whether and how bank-type orientation affects the cost and revenue efficiency of conventional and Islamic banks. They find that post-crisis Islamic banks underperform their conventional counterparts on both accounts within a mixed banking system.

This study did not include comparative data before the 2008 financial crisis. There is also a great deal of heterogeneity among Islamic banks in the samples that have been examined here and by other researchers and the constructed efficiency scores should be interpreted cautiously as divergent Islamic banks are pooled in the same samples.

This study identified factors that may help bank managers to improve their financial outlook by controlling revenue and cost efficiency profitability. These factors could as well help to understand how some indicators affect both cost and revenue efficiency, particularly in Islamic banking. It also seems that tighter restrictions on Islamic bank activities are negatively associated with bank efficiency. Islamic banks that directly compete with their conventional counterparts in the aftermath of the crisis are less efficient on both the cost and revenue frontiers. They are potentially hindered by the differential regulations of supervising authorities in dual banking systems.

The authors provide recommendations regarding regulatory and other issues that are relevant to Islamic banking and further research is suggested. Findings are relevant to a variety of stakeholders (managers, policymakers and regulators). Islamic banking authorities could re-examine the benefits of partially moving to a more standardized/conventional system of banking by lifting some trading restrictions. In addition, developing and maintaining managerial skills is an indispensable instrument for the long-term endurance of any system. A related aspect is thus an effort to determine the holistic efficiency (including managerial) of Islamic banks as a guide for policymakers to improve managerial performance.

There is relatively limited empirical work that investigates the efficiency between Islamic and conventional banking in the aftermath of the crisis in the Gulf region despite the growing importance of this region on political and economic levels. The authors also examine the revenue efficiency measure often under-researched in the literature and particularly important for comparative studies. Overseas-owned banks have attained much higher infiltration levels in middle-eastern countries over the past decade. It has also been suggested that market penetration differences may also be related to bank efficiency concerns among countries and their financial systems as opposed to types of banks.

This study provides valuable insights to managers aiming to increase the effectiveness of their diversification and growth portfolios. The purpose of this paper is to examine the value of utilizing a neural networks (NNs) approach using mergers and acquisition (M&A) data confined in the US technology domain.

Using data from Bloomberg for the period 2000–2016, the results confirm that an NN approach provides more explanation between financial variables in the model than a traditional regression model where the NN approach of this study is then compared with linear classifier, logistic regression. The empirical results show that NN is a promising method of evaluating M&A takeover targets in terms of their predictive accuracy and adaptability.

The findings emphasize the value alternative methodologies provide in high-technology industries in order to achieve the screening and explorative performance objectives, given the technological complexity, market uncertainty and the divergent skill sets required for breakthrough innovations in these sectors.

NN methods do not provide for a fuller analysis of significance for each of the autonomous variables in the model as traditional regression methods do. The generalization breadth of this study is limited within a specific sector (technology) in a specific country (USA) covering a specific period (2000–2016).

Investors value firms before investing in them to identify their true stock price; yet, technology firms pose a great valuation challenge to investors and analysts alike as the latest information technology stock price bubbles, Silicon Valley and as the recent stratospheric rise of financial technology companies have also demonstrated.

Numerous studies have shown that M&As are more often than not destroy value rather than create it. More than 50 percent of all M&As lead to a decline in relative total shareholder return after one year. Hence, effective target identification must be built on the foundation of a credible strategy that identifies the most promising market segments for growth, assesses whether organic or acquisitive growth is the best way forward and defines the commercial and financial hurdles for potential deals.

Technology firm value is directly dependent on growth, consequently most of the value will originate from future customers or products not from current assets that makes it challenging for investors to measure a firm’s beta (risk) where the value of a technology is only known after its commercialization to the market. A differentiated methodological approach used is the use of NNs, machine learning and data mining to predict bankruptcy or takeover targets.

This paper aims to draw on the potential behavioural implications of the new (economic) measurement attributes initiated recently by the International Accounting Standard Board (IASB) in their efforts to reflect more relevant, “true” underlying economic values as opposed to historical.

Owing to lack of readily observable market prices (market values) for loans (retail and commercial operations) for statistical testing and initial conservatism on the part of banks for a survey to be conducted, 15 interviews were employed (from October 2005 to November 2006) with major bankers (CEOs and CFOs of major banks) and standard setters. The paper analyses the perceived benefits and costs associated with the application of two diametrically opposite measurement methodologies for banks. These can also have important implications for the “perceived” value/measurement profile of a bank – as argued in the concluding section – for bankers and their regulators, on the one hand, and accounting standard setters and investors, on the other.

The propositions constitute a significant departure from current accounting practices in that all financial assets and liabilities should uniformly be recognised and reported under a universally accepted “economistic” measurement framework.

The paper captures perceptions and attitudes as to the future “behavioural” direction of banks and provides a balanced argument between the rigours of historical cost accounting and fair value accounting.

This paper attempts to investigate the effect of corruption on the real and accrual earnings management of target firms in the process of mergers and acquisitions.

The sample includes target firms from the European area that participate in mergers or acquisitions announced during 2010–2020. The preliminary empirical part estimates the level of earnings management during the period two years before the deal's announcement to identify whether the sample follows the manipulation behavior that the literature suggests for target firms. The primary empirical analysis focuses on the impact of corruption on real and accrual-based earnings management proxies, employing regression models and two alternative proxies for corruption. The existing literature points out that the combination of low levels of corruption and an integrated legal system reduces earnings manipulation.

The findings provide strong evidence for systematic downwards accounting manipulation practices, whereas the findings for real earnings management are not significant. The findings of the main empirical part show that corruption is positively associated with accrual-based manipulation and negatively related to real earnings management. In essence, in economies with a high level of transparency, managers adopt the manipulation of operating activities as a less detectable practice of earnings management instead of engaging in accounting procedures.

This study contributes to the literature highlighting the diversification of these firms' manipulation strategies according to the national level's corruption status.

This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis.

This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review.

This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward.

While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research.

Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas.

Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society.