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Technology-facilitated abuse, so-called “tech abuse,” through phones, trackers, and other emerging innovations, has a substantial impact on the nature of intimate partner violence (IPV). The current chapter examines the risks and harms posed to IPV victims/survivors from the burgeoning Internet of Things (IoT) environment. IoT systems are understood as “smart” devices such as conventional household appliances that are connected to the internet. Interdependencies between different products together with the devices' enhanced functionalities offer opportunities for coercion and control. Across the chapter, we use the example of IoT to showcase how and why tech abuse is a socio-technological issue and requires not only human-centered (i.e., societal) but also cybersecurity (i.e., technical) responses. We apply the method of “threat modeling,” which is a process used to investigate potential cybersecurity attacks, to shift the conventional technical focus from the risks to systems toward risks to people. Through the analysis of a smart lock, we highlight insufficiently designed IoT privacy and security features and uncover how seemingly neutral design decisions can constrain, shape, and facilitate coercive and controlling behaviors.

The purpose of this paper is to measure Chinese industries’ eco-efficiency during 2006-2013. The Chinese industry attained rapid achievement in recent decades, but meanwhile, overconsumption of energy and environmental pollution have become serious problems. To solve these problems, many research studies used the data envelopment analysis (DEA) to measure the Chinese industry’s eco-efficiency. However, because the target set by these works is usually the furthest one for a province to be efficient, it may hardly be accepted by any province.

This paper builds a new “closest target method” based on an additive DEA model considering the undesirable outputs. This method is a mixed-integer programming problem which can measure the ecological efficiency of provinces and more importantly guide the province to perform efficiently with minimum effort.

The results show that the eco-efficiency of Chinese provinces increased at the average level, but the deviations remained at a larger value. Compared to the “furthest” target methods, the targets by the approach proposed by this study are more acceptable for a province to improve its performance on both economy and environment counts.

This study is the first attempt to introduce the closest targets concept to measure the eco-efficiency and set the target for each provincial industry in China.

This paper covers the development of a multidimensional contract administration performance model (CAPM) for construction projects. The proposed CAPM is intended to be used by the industry stakeholders to measure the construction contract administration (CCA) performance and identify the strengths and weaknesses of the CCA system for running or completed projects.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. In the first phase, contract administration indicators were collected from relevant literature. In the second phase, an online questionnaire was prepared, and data were collected and analyzed using the crisp value of fuzzy membership function, and structural equation modeling (SEM). The fuzzy set was chosen for this study due to the presence of uncertainty and fuzziness associated with the importance of several key indicators affecting the CCA performance. Finally, SEM was used to test and analyze interrelationships among constructs of CCA performance.

The data collected from 336 construction professionals worldwide through an online survey was utilized to develop the fuzzy structural equation model. The goodness-of-fit and reliability tests validated the model. The study concluded a significant correlation between CCA performance, CCA operational indicators, and the process groups.

The contribution of this paper to the existing knowledge is the development of a fuzzy structural equation model that serves as a measurement tool for the contract administration performance. This is the first quantitative structural equation model to capture contract administration performance. The model consists of 93 Construction Contract Administration(CCA) performance indicators categorized into 11 project management process groups namely: project governance and start-up; team management; communication and relationship management; quality and acceptance management; performance monitoring and reporting management; document and record management; financial management; changes and control management; claims and dispute resolution management; contract risk management and contract closeout management.

The purpose of this paper is to investigate whether management strategies implemented by non-commercial traders may be identified as a key factor in affecting oil price paths in the conventional pre- and post-financialization periods.

By using a vector autoregressive approach the dynamic analysis of the daily stock indexes for some of the most important world economies and the oil prices is conducted starting from 1992 to the end of 2020.

The findings do not support the idea that the financial markets act as a privileged conduit in transmitting the shocks to the oil spot quotations.

Such a direct assessment has not been previously proposed in literature wherein – under a financial perspective – the returns are generally taken into consideration.

The purpose of this stud is to analyze the financialization effect on oil prices.

This study applied the technique of multibreak point analysis with Bai and Perron test plus VAR methodology.

Findings revealed that there was no effect on oil prices.

To the best of the author’s knowledge, this is the first paper combining the multibreakpoint analysis with VAR for the period analyzed in the present work.

The unprecedented SARS-CoV-2 (COVID-19) pandemic has further constrained the budgets of governments worldwide for delivering their much-needed infrastructure. Consequently, public-private partnerships (PPPs), with the private sector's investment and ingenuity, would appear to be an increasingly popular alternative. Value for money (VfM) has become the major criterion for evaluating PPPs against the traditional public sector procurement and, however, is plagued with controversy. Hence, it is important that governments compare and contrast their practice with similar and disparate bodies to engender best practice. This paper, therefore, aims to understand governments' assessment context and provide a cross-continental comparison of their VfM assessment.

Faced with different domestic contexts (e.g. aging infrastructure, population growth, and competing demands on finance), governments tend to place different emphases when undertaking the VfM assessment. In line with the theory of boundary spanning, a cross-continental comparison is conducted between three of the most noticeable PPP markets (i.e. the United Kingdom, Australia and China) about their VfM assessment. The institutional level is interpreted by a social, economic and political framework, and the methodological level is elucidated through a qualitative and quantitative VfM assessment.

There are individual institutional characteristics that have shaped the way each country assesses VfM. For the methodological level, we identify that: (1) these global markets use a public sector comparator as the benchmark in VfM assessment; (2) ambiguous qualitative assessment is conducted only against PPPs to strengthen their policy development; (3) Australia's priority is in service provision whereas that of the UK and China is project finance and production; and (4) all markets are seeking an amelioration of existing controversial VfM assessments so that purported VfM relates to project lifecycles. As such, an option framework is proposed to make headway towards a sensible selection of infrastructure procurement approaches in the post COVID-19 era.

This study addresses a current void of enhancing the decision-making process for using PPPs within today's changing environment and then opens up an avenue for future empirical research to examine the option framework and ensuing VfM decisions. Practically, it presents a holistic VfM landscape for public sector procurers that aim to engage with PPPs for their infrastructure interventions.

The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.

The purpose of this study was to explore how variables such as student demographics, pre-college leadership activities, and perceived pre-college parenting behaviors predict students’ leader self-efficacy (i.e., individuals’ confidence in themselves to lead and belief that others will support their leadership [Hannah et al., 2008]) in college and leader emergence (i.e., college-based leadership involvements [DeRue & Ashford, 2010]) in college. Undergraduate students (n = 420) at a large, public university in the Mid-Atlantic were surveyed to examine these relationships and data were analyzed using hierarchical and logistic regression, with appropriate controls and moderators. Findings included discovery that pre-college engagement with sports team positional leadership, community service, extracurriculars, and positive parenting behaviors, such as family routine and greater quality time with parents, predicted leader self-efficacy. Further, findings noted that pre-college community service, extracurriculars, peer tutoring and perceptions of parental quality time and proactive parenting predicted leader emergence. This study suggests that students’ leadership development is influenced by myriad systems across the lifespan and demonstrates that, as educators committed to student development, we must engage the full arc of our students’ leadership journeys and provide for intentional partnerships between higher education and the K-12 community.

This study aims to provide a comprehensive framework for the study of indoor air quality (IAQ) in hospitality premises. The goal is to identify the drivers of air pollution, both at the exogenous and endogenous level, to generate insights for facility managers.

The complexity of hospitality premises requires an integrated approach to properly investigate IAQ. The authors develop an overarching framework encompassing a monitoring method, based on real-time sensors, a technological standard and a set of statistical analyses for the assessment of both IAQ performance and drivers, based on correlation analyses, analysis of variance and multivariate regressions.

The findings suggest that the main drivers of IAQ differ depending on the area monitored: areas in contact with the outdoors or with high ventilation rates, such as halls, are affected by outdoor air quality more than guestrooms or fitness areas, where human activities are the main sources of contamination.

The results suggest that the integration of IAQ indicators into control dashboards would support management decisions, both in defining protocols to support resilience of the sector in a postpandemic world and in directing investments on the premises. This would also address guests’ pressing demands for a broader approach to cleanliness and safety and support their satisfaction and intention to return.

To the best of the authors’ knowledge, this is the first study developing a comprehensive framework to systematically address IAQ and its drivers, based on a standard and real-time monitoring. The framework has been applied across the longest period of monitoring for a hospitality premise thus far and over an entire hotel facility.