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Web traffic is doubling every year, according to recent global studies. The user needs more information from Web sites and wants to spend as little time for downloading as possible. Simultaneously, more Internet bandwidth is needed and all ISPs are trying to build high bandwidth networks. This paper presents a case study that calculates the reduction of the time needed for a Web page to be fully downloaded and delivered to the user. Presents a way to calculate the reduction of data transfer, bandwidth resources and response time when the HTTP/1.1’s compressing feature is enabled (either in plain hypertext files or the text output of CGI programs or dynamically generated pages). Measurements are taken from five popular Web sites in order to validate our statement for reduction in transfer time. The definition of the mean size of a Web page that commercial Web sites have is additionally in the scope of this paper.

Aims to present the work done in the development of a simplified office suite for disabled and focus on the use of technology applied to the area of “designing for all”. The paper presents an overview of the state‐of‐the‐art in the design for all. It provides practical references to techniques used. The main scope of the paper is to explore the developed technology and give details for the adopted mechanisms. It provides information about designing and implementing software applications for disabled and present a case study for mentally disabled. The paper presents a system that can be used by a specific target group. For this reason, it should be used as reference point for this group, although several techniques can be used for other user categories. The paper is a very useful presentation of an actual system that has been designed and implemented to cover the needs of disabled, useful for interaction with designers and researchers in assistive technology, and it fulfils the need for demonstrative technology in the area of designing for all.

Although waiting on the Internet is widely recognised as a crucial factor in the evolution of e‐commerce and the Internet in general, it is not a widespread topic of research. This article identifies and reviews 21 papers based on 13 separate empirical studies on waiting on the Internet. The literature draws from the areas of marketing, system response time studies and quality of service studies. Having reviewed the existing literature, this article proposes an agenda for future research. Recommendations are made to extend the range of research methodologies applied to this topic, to broaden the definition of waiting on the Internet to include other forms of online waiting, and to continue the interdisciplinary approach to research on online waiting.

This study aims to explore the perception of algorithm accuracy among data professionals in higher education.

Social justice theory guided the qualitative descriptive study and emphasized four principles: access, participation, equity and human rights. Data collection included eight online open-ended questionnaires and six semi-structured interviews. Participants included higher education professionals who have worked with predictive algorithm (PA) recommendations programmed with student data.

Participants are aware of systemic and racial bias in their PA inputs and outputs and acknowledge their responsibility to ethically use PA recommendations with students in historically underrepresented groups (HUGs). For some participants, examining these topics through the lens of social justice was a new experience, which caused them to look at PAs in new ways.

Small sample size is a limitation of the study. Implications for practice include increased stakeholder training, creating an ethical data strategy that protects students, incorporating adverse childhood experiences data with algorithm recommendations, and applying a modified critical race theory framework to algorithm outputs.

The study explored the perception of algorithm accuracy among data professionals in higher education. Examining this topic through a social justice lens contributes to limited research in the field. It also presents implications for addressing racial bias when using PAs with students in HUGs.

The purpose of this paper is to explore farmers’ intentions to engage in food brand development schemes and identify the factors that impact this.

By assuming the utility maximization behavior of farmers, based on data collected from 539 fruit and vegetable producers, this study estimates an intention to participate and a willingness to pay model.

Three groups of factors determine the utility the farmers derive and subsequently their engagement in brand development. Farm business characteristics include farmers’ age, the attainment of quality certification and cultivated area, while psychological factors include farmers’ attitudes toward local reflections of the brand, perceptions regarding the need for farm business external support and consumers’ interest, as well as farmers’ commitment to quality requirements. Farmers’ strategies related to the share of products sold by cooperatives and to individually use the brand also determine their engagement in a brand development scheme.

Future research should distinguish producers according to the marketing channel they choose and their industry, and explore the intentions of intermediate marketers.

Marketing cooperatives should undertake initiatives to develop local brands effectively, taking into consideration the factors that impact farmers’ engagement, while food marketing firms should properly adapt their purchasing and promotion strategies. Public authorities should formulate a policy mix that enhances farmers’ knowledge related to marketing issues and encourages farmers to strengthen their positions in the marketplace.

The research reveals a strategic proactive behavior of farmers favoring the development of local brands, and provides insights into the factors that impact farmers’ adoption decisions.

The Internet and social media have fundamentally transformed the ways in which individuals find jobs. Relatively little is known about how demand-side market actors use online information and the implications for social stratification and mobility. This study provides an in-depth exploration of the online recruitment strategies pursued by human resource (HR) professionals. Qualitative interviews with 61 HR recruiters in two southern US metro areas reveal two distinct patterns in how they use Internet resources to fill jobs. For low and general skill work, they post advertisements to online job boards (e.g., Monster and CareerBuilder) with massive audiences of job seekers. By contrast, for high-skill or supervisory positions, they use LinkedIn to target passive candidates – employed individuals who are not looking for work but might be willing to change jobs. Although there are some intermediate practices, the overall picture is one of an increasingly bifurcated “winner-take-all” labor market in which recruiters focus their efforts on poaching specialized superstar talent (“purple squirrels”) from the ranks of the currently employed, while active job seekers are relegated to the hyper-competitive and impersonal “black hole” of the online job boards.

This book aimed to conceptualise a construction workforce management model suitable for effectively managing workers in construction organisations. To this end, this chapter presents the conceptualised model, which consists of seven workforce management practices with their respective measurement variables. Drawing from existing theories, models, and practices, the chapter concludes that a construction organisation that will attain its strategic objectives in the current fourth industrial revolution era must be willing to promote effective recruitment and selection, compensation and benefits, performance management and appraisal, employee involvement and empowerment, training and development, as well as improving workers emotional intelligence and handling external environment pressure. These practices can promote proactiveness, participation, and improved skills and can lead to effective commitment, better quality, and flexibility within the organisation.

Introduction: Human resource management (HRM) is going through a transformation phase due to the pandemic. The COVID-19 crisis compelled the employees to work virtually. To mitigate the effects of COVID-19, several organisations heavily invested in artificial intelligence (AI) in the realm of HRM.

Purpose: With limited studies on the paradigm shift in HRM post-pandemic and the role of AI, the study investigates and proposes a conceptual framework for the paradigm shift in HRM practices post-COVID-19 pandemic and the significance of AI. Furthermore, the study investigates the outcomes of the use of AI in HRM for organisations and employees.

Methodology: A comprehensive review of the literature based on the guidelines of Tranfield, Denyer, and Smart (2003) and Crossan and Apaydin (2010) has been followed. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes involved.

Findings: COVID-19-related economic disruption has led to a paradigm shift in HRM practices. AI-enabled HRM practices are now centred around remote and contingent workforce management, mindfulness, social capital, increasing employee engagement, reskilling and upskilling towards new competencies, etc. AI is making remote work seamless through smooth recruitment and selection process, onboarding, career and development, tracking and managing the performance, facilitating learning, and talent management. Post-pandemic, AI-powered tools based on data mining (DM), predictive analytics, big data analytics, natural language processing (NLP), intelligent robots, machine learning (ML), virtual (VR)/augmented reality (AR), etc., have paved the way for managing the HRM practices effectively, thereby leading to enhanced organisational performance, employee well-being, automation, and reduced cost.

The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

The purpose of this paper is to present a novel approach for recruiting and ranking job applicants in online recruitment systems, with the objective to automate applicant pre‐screening. An integrated, company‐oriented, e‐recruitment system was implemented based on the proposed scheme and its functionality was showcased and evaluated in a real‐world recruitment scenario.

The proposed system implements automated candidate ranking, based on objective criteria that can be extracted from the applicant's LinkedIn profile. What is more, candidate personality traits are automatically extracted from his/her social presence using linguistic analysis. The applicant's rank is derived from individual selection criteria using analytical hierarchy process (AHP), while their relative significance (weight) is controlled by the recruiter.

The proposed e‐recruitment system was deployed in a real‐world recruitment scenario, and its output was validated by expert recruiters. It was found that with the exception of senior positions that required domain experience and specific qualifications, automated pre‐screening performed consistently compared to human recruiters.

It was found that companies can increase the efficiency of the recruitment process if they integrate an e‐recruitment system in their human resources management infrastructure that automates the candidate pre‐screening process. Interviewing and background investigation of applicants can then be limited to the top candidates identified from the system.

To the best of the authors’ knowledge, this is the first e‐recruitment system that supports automated extraction of candidate personality traits using linguistic analysis and ranks candidates with the AHP.