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The construction industry, being one of the main activities in the ever-demanding need for technology developments, sometimes falls short of other industries in terms of implementation. The adoption of Big Data (BD) in industries such as health and retail has had positive impacts in aspects such as decision-making processes and forecasting trends that allow planning some future business movements. Hence, the question of whether these results can be imitated in the construction industry. Therefore, this paper aims to address the level of awareness identified as a first step towards implementation of the BD concept within the construction industry in the Dominican Republic (DR).

As little to no information exist on the subject; the selected approach to perform this research was qualitative methodology; 21 semi-structured interviews were studied using situational awareness. Four levels of awareness were developed based on the Endsley’s Situation Awareness model.

The results showed that nearly 95% of the interviewees had either no knowledge or very basic awareness of the BD requirements or intermediate awareness, but only 5% had applied BD concepts in the construction industry.

This study shows the gaps that exist in the understanding and implementation of BD concepts in the DR construction industry. This paper establishes the need to develop continuous professional development programmes for construction professionals and a need to update curriculum in construction-related education.

Wages have been spreading out across workers over time – or in other words, the 90th/50th wage ratio has risen over time. A key question is, has the productivity distribution also spread out across worker skill levels over time? Using our calculations of productivity by skill level for the United States, we show that the distributions of both wages and productivity have spread out over time, as the right tail lengthens for both. We add Organization for Economic Co-Operation and Development (OECD) countries, showing that the wage–productivity correlation exists, such that gains in aggregate productivity, or GDP per person, have resulted in higher wages for workers at the top and bottom of the wage distribution. However, across countries, those workers in the upper-income ranks have seen their wages rise the most over time. The most likely international factor explaining these wage increases is the skill-biased technological change of the digital revolution. The new artificial intelligence (AI) revolution that has just begun seems to be having similar skill-biased effects on wages. But this current AI, called “supervised learning,” is relatively similar to past technological change. The AI of the distant future will be “unsupervised learning,” and it could eventually have an effect on the jobs of the most highly skilled.

The Bureau of Economics in the Federal Trade Commission has a three-part role in the Agency and the strength of its functions changed over time depending on the preferences and ideology of the FTC’s leaders, developments in the field of economics, and the tenor of the times. The over-riding current role is to provide well considered, unbiased economic advice regarding antitrust and consumer protection law enforcement cases to the legal staff and the Commission. The second role, which long ago was primary, is to provide reports on investigations of various industries to the public and public officials. This role was more recently called research or “policy R&D”. A third role is to advocate for competition and markets both domestically and internationally. As a practical matter, the provision of economic advice to the FTC and to the legal staff has required that the economists wear “two hats,” helping the legal staff investigate cases and provide evidence to support law enforcement cases while also providing advice to the legal bureaus and to the Commission on which cases to pursue (thus providing “a second set of eyes” to evaluate cases). There is sometimes a tension in those functions because building a case is not the same as evaluating a case. Economists and the Bureau of Economics have provided such services to the FTC for over 100 years proving that a sub-organization can survive while playing roles that sometimes conflict. Such a life is not, however, always easy or fun.

This paper aims to explore the current condition of the Big Data concept with its related barriers, drivers, opportunities and perceptions in the architecture, engineering and construction (AEC) industry with an emphasis on facilities management (FM).

Following a comprehensive literature review, the Big Data concept was investigated through two scoping workshops with industry experts and academics.

The value in data analytics and Big Data is perceived by the industry, yet the industry needs guidance and leadership. Also, the industry recognises the imbalance between data capturing and data analytics. Large IT vendors’ developing AEC industry-focused analytics solutions and better interoperability among different vendors are needed. The general concerns for Big Data analytics mostly apply to the AEC industry as well. Additionally, however, the industry suffers from a structural fragmentation for data integration with many small-sized companies operating in its supply chains. This paper also identifies a number of drivers, challenges and way-forwards that calls for future actions for Big Data in FM in the AEC industry.

The nature of data in the business world has dramatically changed over the past 20 years. This phenomenon is often broadly dubbed as “Big Data” with its distinctive characteristics, opportunities and challenges. Some industries have already started to effectively exploit “Big Data” in their business operations. However, despite many perceived benefits, the AEC industry has been slow in discussing and adopting the Big Data concept. Empirical research efforts investigating Big Data for the AEC industry are also scarce. This paper aims at outlining the benefits, challenges and future directions (what to do) for Big Data in the AEC industry with an FM focus.

The purpose of this study is to expand on the existing literature by specifically examining data security incidents within the hospitality industry, assessing origins and causes, comparing breaches within the industry with those of other industries and identifying areas of concern.

A sample of data breach incidents is drawn from the Verizon VERIS Community Database (VCDB). Statistical comparisons between hospitality and non-hospitality industry firms are conducted following the Verizon A4 threat framework.

The results reveal that breaches between hospitality and non-hospitality firms differ significantly in terms of actors, actions, assets and attributes. Specifically, proportions of breaches in the hospitality industry are larger in terms of external actors, hacking and malware, user devices compromised and integrity violations. Additionally, compared to other industries, point-of-sales (POS) system breaches occur at a higher rate in the hospitality industry. The study finds that company size, hacking and malware predict the likelihood of a POS breach.

The study uses secondary data and does not include the entire universe of data breaches.

In the quest to reduce data breach incidents, it is imperative to identify and assess the nature of data breach incidents between industries. Doing so permits the development of targeted industry-specific solutions rather than generic ones. This study systematically identifies differences between hospitality and non-hospitality data security incidents and then suggests areas where hospitality companies should focus future attention to mitigate breach incidents.

本论文延展了现有文献, 检测了酒店业中的数据安全事故, 评估其起因, 比较其他产业和酒店产业数据泄露的区别, 以及找出关键区域。

样本数据为 Verizon VERIS 社区数据库（VCDB）中的数据泄露事件。研究遵循Verizon A4 危险模型, 对酒店业和非酒店业之间事件进行了数据分析比较。

研究结果表明酒店公司和非酒店公司的数据泄露在当事人、行为、资产、和属性方面, 有着很大不同。其中, 酒店业中的数据泄露比例在外部因素、黑客、病毒、用户端失灵、和违反道德方面比较大。此外, 相对其他产业, POS系统在酒店产业中的数据泄露概率较高。本论文发现公司规模、黑客、和病毒对POS数据泄露的影响有着重大决定作用。

本论文使用二手数据, 并未检测整体数据泄露数据。

为了减少数据泄露事件, 产业之间数据泄露事件属性的认定和评价至关重要。因此, 可以针对具体产业具体事件制定出特定的解决方案。本论文系统上指出了酒店和非酒店业的数据安全事件的区别, 以及指出哪些方面, 酒店业应该重点关注, 以减少未来数据泄露事件。

Data communications is a relatively neglected topic, which is surprising as it offers the infrastructural platform that can help to provide organizations with flexibility and responsiveness. DataComms 2000 explores the business themes and issues, and technology drivers and focus that will shape the industry. Explores the perspectives of a range of different actors.

The purpose of this paper is to examine the challenges to data sharing among construction stakeholders in the South African construction industry and also assess stakeholders’ perceptions of the benefits of data sharing.

This study is a cross-sectional survey administered via a Web-based online survey on construction professionals registered with the South African Council for the Project and Construction Management Professions (SACPCMP). The respondents rated on a five-point Likert scale the level of influence of the challenges of, and the benefits derivable from data sharing. These were analysed using descriptive and inferential statistical techniques.

The results of the principal component analysis (PCA) presented a five-factor structure of the challenges to data sharing, including reporting context/framework/lack of expertise, cost considerations/clients’ influences, data interoperability, stakeholders conservative attitude and personal interest/data confidentiality. These have percentage variances 17.124%, 16.929%, 13.786%, 13.353% and 12.961%, respectively. For the benefits of data sharing, the constructs were categorized into four themes, namely, optimal project decisions/stakeholders’ confidence, benchmarking/ collaboration among firms, time and cost benefits and enhanced market intelligence. These have respective variances of 24.598%, 18.393%, 16.160% and 14.685%.

It is expected that this study will provide information to stakeholders towards implementation policies and practices that could eliminate the challenges to data sharing and assemblage, thereby enhancing the level of data sharing in the construction industry.

Given the increasing global and technological changes, it might be expected that there will be an increased appeal by construction stakeholders towards embracing data sharing and assemblage owing to the inherent benefits and value.

The purpose of this paper is to present an analysis of the development of a new graduate certificate course in Petroleum Data Management. The course was developed in response to an identified gap in skills and training in data management that was perceived to be a substantial risk in terms of: industry sustainability, efficiency and potentially wider implications of safety as assets are transferred between operators and for decommissioning. The aim of this paper is to critically reflect on how academia and industry can work together to support emerging professions in information management.

The paper draws on observations and interviews from key stakeholders involved in the course development.

The course development process was ultimately successful but also challenging and lessons have been learned which will be of interest to the wider professional and academic body. These include: securing resources and industry engagement for course development, negotiating cultural differences between academic and industry and managing stakeholder relationships throughout the lifecycle of the course development.

The paper demonstrates the challenges and opportunities of developing a university course in collaboration with industry partners. Oil and gas exploration and production is a data-intensive industry but it was only relatively recently that attempts have been made to set industry standards and roles of “data manager” or “data analyst” have been created to manage these. This paper has wider implications for understanding the professionalisation of the nascent data management disciplines and contributes to the ongoing dialogue around the changing library and information science profession.

This study aims to explore the findings related to data literacy skills for students to succeed in the digital age labor market and the role of university-industry collaborations (UICs) in the co-design and co-delivery of curriculum for the development of students’ data literacy.

The study uses an interview-based research methodology to gather insights from industry partners and stakeholders. The interviews focus on identifying key data literacy skills, understanding the significance of these skills and exploring the role of UICs in enhancing students’ data literacy.

The findings reveal several important data literacy skills for students. The most commonly mentioned skills include data evaluation/analysis, identifying the relevance of data and data protection in a sensitive manner. Participants also emphasized the importance of recognizing the interrelationships among data, adapting data across different contexts and strategically combining diverse data. The study emphasizes the role of universities in providing a well-rounded educational setting that fosters the development of data literacy skills. Additionally, it highlights the value of practical collaborations between universities and industries, enabling students to apply theoretical knowledge in real-world contexts.

The study highlights the interconnected nature of various data skills and emphasizes the significance of data literacy in navigating the complexities of the digital age labor market. It also sheds light on the role of UICs in codesigning and codelivering curricula to enhance students’ data literacy. The findings provide valuable insights into the practical implications for UICs in preparing students for the data-driven job market.

Presents a case study demonstrating financial statement ratio analysis (FSRA). This analysis matches company to industry data and builds sales forecasting models. FSRA imputes forecast standards of sales and costs, and applies them to a budgeted financial statement variance analysis for the EE (electronic and electrical) industry. Develops the concept of industry base standards, integrating them into the more traditional statistical and accounting concepts of quality control standards. Provides an implementation example, and reviews possible improvements to the current methodology and approach. Uses a similar methodology to forecast the stock market value with some exceptions. Models sales and costs of an individual company and an industry based largely on aggregate industry databases. For this purpose, uses a multivariate linear trend regression analysis for the sales forecasting model. Defines and tests related hypotheses and evaluates their significance and confidence levels. For an illustration uses the EE industry and the APM company. Also demonstrates a microcomputer‐based FSRA software that speeds, facilitates, and helps to accomplish the stated objectives. The FSRA software uses industry financial statement databases, computes financial ratios and builds forecasting models.