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The issue of model selection in applied research is of vital importance. Since the true model in such research is not known, which model should be used from among various potential ones is an empirical question. There might exist several competitive models. A typical approach to dealing with this is classic hypothesis testing using an arbitrarily chosen significance level based on the underlying assumption that a true null hypothesis exists. In this paper, the authors investigate how successful the traditional hypothesis testing approach is in determining the correct model for different data generating processes using time series data. An alternative approach based on more formal model selection techniques using an information criterion or cross-validation is also investigated.

Monte Carlo simulation experiments on various generating processes are used to look at the response surfaces resulting from hypothesis testing and response surfaces resulting from model selection based on minimizing an information criterion or the leave-one-out cross-validation prediction error.

The authors find that the minimization of an information criterion can work well for model selection in a time series environment, often performing better than hypothesis-testing strategies. In such an environment, the use of an information criterion can help reduce the number of models for consideration, but the authors recommend the use of other methods also, including hypothesis testing, to determine the appropriateness of a model.

This paper provides an alternative approach for selecting the best potential model among many for time series data. It demonstrates how minimizing an information criterion can be useful for model selection in a time-series environment in comparison to some standard hypothesis testing strategies.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting‐edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

The growth of digital piracy, in the wake of the technological revolution during the last few decades, is as inevitable as night following day. Where innovators have gone first, pirates and hackers have followed. Many of these people are at least as smart as their targets and therein lies one of the major problems afflicting companies: how best, if indeed at all, to take on the hackers.

The paper provides strategic insights and practical thinking that have influenced some of the world's leading organizations.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy‐to‐digest format.

In all existing theoretical papers on causality it is assumed that the lag length is known a priori. However, in applied research the lag length has to be selected before testing for causality. The purpose of this paper is to suggest that in investigating the effectiveness of various Granger causality testing methodologies, including those using bootstrapping, the lag length choice should be endogenized, by which we mean the data‐driven preselection of lag length should be taken into account.

The size and power of a bootstrap test with endogenized lag‐length choice are investigated by simulation methods. A statistical software component is produced to implement the test, which is available online.

The simulation results show that this test performs well. An application of the test provides empirical support for the hypothesis that the UAE financial market is integrated with the US market.

The empirical results based on this test are expected to be more precise.

This paper considers a bootstrap test for causality with endogenous lag order. This test has superior properties compared to existing causality tests in terms of size, with similar if not better power and it is robust to ARCH effects that usually characterize financial data. Practitioners interested in causal inference based on time series data might find the test valuable.

The purpose of this study is to find funding sources for social health insurance in Sudan to cover vulnerable families.

The concept of this paper is to look into the causality relationship between insured and directly paid poor people and their incomes through using the autoregressive distributed lag model (ARDL) and using a survey raw data carried out in 2010 in Sudan.

The findings show that insured vulnerable people and income-generating activities have a significant positive relationship. The results, on the other hand, show a marginally negative effect on income for those who have directly paid poor people.

Previous research on the position of zakat has focused primarily on its role in poverty alleviation and has given little consideration to its role in social security. This study is characterized by improving the possibility of changing the mechanism of distributing zakat funds in favor of health financing to maximize the benefit.

This paper aims to examine the impact of the existing information and communication technology (ICT) infrastructure and the development of the destination’s ICT on the tourism demand of international tourists in an emerging economy, Vietnam.

Using time-series data from 1995 to 2019, this study applies vector error correction model to analyse the impact of ICT infrastructure in the short- and long term.

The results of analysis show that although ICT infrastructure does not affect the number of international tourists in the short term, it positively contributes to tourism development in the long term. In addition, the results also show that in the short term, consumer prices have a negative impact on tourist arrivals while having a positive effect in the long run.

This study only considers the impact of ICT infrastructure on the whole without going into each factor reflecting different aspects of the ICT infrastructure. Moreover, this research only stops at the pre-pandemic period, so it has not shown the role of ICT infrastructure in travel and tourism demand during severe pandemic periods.

The research results are an essential basis to support the Vietnamese Government’s strategy to pursue an accelerated investment policy in ICT infrastructure, especially investment in the tourism and hotel industries. On the other hand, the research results also create more motivation and confidence for managers and operators of destinations in Vietnam to invest in ICT infrastructure and apply ICT in management.

This study adds to the literature on tourism–ICT linkages in an emerging tourism market directly between ICT infrastructure and international arrivals with a dynamic time series–based approach that considers the dynamics in the tourist demand identification model. In addition, this study used consumer price index to assess the impact of price on tourist demand instead of using the exchange rate or using the relative prices between the origin and destination countries.

本文探讨了现有 ICT 基础设施和目的地 ICT 发展对新兴经济体越南国际游客旅游需求的影响。

该研究使用 1995 年至 2019 年的时间序列数据, 应用矢量纠错模型 (VECM) 来分析 ICT 基础设施的短期和长期影响。

分析结果表明, 虽然ICT基础设施在短期内不会影响国际游客数量, 但从长远来看对旅游业发展有积极贡献。此外, 研究结果还表明, 在短期内, 消费价格对游客人数产生了负面影响, 而从长期来看, 则产生了积极影响。

研究结果是支持越南政府加快信息通信技术基础设施投资政策战略的重要基础, 特别是对旅游业和酒店业的投资。另一方面, 研究成果也为越南旅游目的地的管理者和经营者投资ICT基础设施和在管理中应用ICT创造了更多动力和信心。

本研究通过考虑旅游需求识别模型中的动态的基于动态时间序列的方法, 增加了有关新兴旅游市场中旅游与 ICT 联系的文献, 该联系直接在 ICT 基础设施和国际入境者之间进行。此外, 该研究使用 CPI 来评估价格对游客需求的影响, 而不是使用汇率或使用来源国和目的地国之间的相对价格。

Outlines the experience of the University of Alabama when a hacker bypassed the preventative security measures of the library management system and accessed the operating system. Discusses how the break‐in occurred, how the hacker was identified and what actions were taken. Concludes that each system will have to decide what direction to take if such a break‐in occurred, suggesting measures such as employing a full‐time investigator for hackers.

This chapter examines the phenomenon of doxxing: the practice of publishing private, proprietary, or personally identifying information on the internet, usually with malicious intent. Undertaking a scoping review of research into doxxing, we develop a typology of this form of technology-facilitated violence (TFV) that expands understandings of doxxing, its forms and its harms, beyond a taciturn discussion of privacy and harassment online. Building on David M. Douglas's typology of doxxing, our typology considers two key dimensions of doxxing: the form of loss experienced by the victim and the perpetrator's motivation(s) for undertaking this form of TFV. Through examining the extant literature on doxxing, we identify seven mutually non-exclusive motivations for this form of TFV: extortion, silencing, retribution, controlling, reputation-building, unintentional, and doxxing in the public interest. We conclude by identifying future areas for interdisciplinary research into doxxing that brings criminology into conversation with the insights of media-focused disciplines.

This chapter explores collective information processing among black-hat hackers during their crises events. The chapter presents a preliminary study on one of Tor-based darknet market forums, during the shutdowns of two cryptomarkets. Content and network analysis of forum conversations showed that black-hat users mostly engaged with rational information processing and were adept at reaching collective solutions by sharing security advices, new market information, and alternative routes for economic activities. At the same time, the study also found that anti-social and distrustful interactions were aggravated during the marketplace shutdowns. Communication network analysis showed that not all members were affected by the crisis events, alluding to a fragmented network structure of black-hat markets. The chapter concludes that, while darknet forums may constitute resilient, solution-oriented users, market crises potentially make the community vulnerable by engendering internal distrust.

During the past decade, enterprise resource planning (ERP) system implementations have exponentially grown within first large and then small- and medium-sized enterprises. Contemporary implementations, often through application service providers (ASPs), increase already existing pressures to adopt the embedded “best practices” that have been incorporated into the ERP software. The result is the rapid spread of generic business processes enabled through one of only a handful of leading ERP packages. This chapter focuses on the extant research on biodiversity and its focus on the negative effects of monoculture strategies – that is, the focus on a single crop (system) versus a diversity of crops (systems). The biodiversity research establishes a clear pattern of deleterious effects resulting from the vulnerabilities of monoculture strategies. These patterns are mirrored in the ERP environment as vulnerabilities loom from the diminution of diverse business processes, limited adaptability to business environment changes given technology-driven/enabled processes, and increased susceptibility to widespread parasite damage through cyber-attacks. The implications of the study raise questions as to the sustainability of accounting systems, the business environment, and society as a whole from the rapid implementation of sterilized business processes and uniformly vulnerable enterprise software.

This paper aims to examine optimal decisions for information security investments for a firm in a fuzzy environment. Under both sequential and simultaneous attack scenarios, optimal investment of firm, optimal efforts of attackers and their economic utilities are determined.

Throughout the analysis, a single firm and two attackers for a “firm as a leader” in a sequential game setting and “firm versus attackers” in a simultaneous game setting are considered. While the firm makes investments to secure its information assets, the attackers spend their efforts to launch breaches.

It is observed that the firm needs to invest more when it announces its security investment decisions ahead of attacks. In contrast, the firm can invest relatively less when all agents are unaware of each other’s choices in advance. Further, the study reveals that attackers need to exert higher effort when no agent enjoys the privilege of being a leader.

In a novel approach, inherent system vulnerability of the firm, financial benefit of attackers from the breach and monetary loss suffered by the firm are considered, as fuzzy variables in the well-recognized Gordon – Loeb breach function, with the help of fuzzy expectation operator.

This study reports that the optimal breach effort exerted by each attacker is proportional to its obtained economic benefit for both sequential and simultaneous attack scenarios. A set of numerical experiments and sensitivity analyzes complement the analytical modeling.

In a novel approach, inherent system vulnerability of the firm, financial benefit of attackers from the breach and monetary loss suffered by the firm are considered, as fuzzy variables in the well-recognized Gordon – Loeb breach function, with the help of fuzzy expectation operator.