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Numerous research questions in e-HRM research are directly related to the usage of diverse information systems by HR professionals, line managers, employees, and/or applicants. Since they are regularly based on Internet technologies, information systems in e-HRM automatically store detailed usage data in log files of web servers. Subsumed as “web mining,” such data are frequently used as inputs for innovative data analysis in e-commerce practice. Though also promising in empirical e-HRM research, web mining is neither discussed nor applied in this area at present. Our chapter therefore aims at a methodological evaluation of web mining as an e-HRM research approach. After introducing web mining as a possible approach in e-HRM research, we examine its applicability by discussing available data, feasible methods, coverable topics, and confirmable privacy. Subsequently, we classify the approach methodologically by examining major issues. Our evaluation reveals that “web mining” constitutes a promising additional research approach that enables research to answer numerous relevant questions related to the actual usage of information systems in e-HRM.

Purpose — The overall quality of an information retrieval system depends on many different aspects of the system and its users' information seeking behaviour, such as the speed of the system, the user interface, the query language and the features provided by the engine. One of the most important aspects is the effectiveness of the retrieval system, i.e. its ability to retrieve items that are relevant to the information need of an end user. This chapter focuses on methods for measuring effectiveness, in particular focusing on recent work that more directly models the utility of an engine to its users.

Methodology/approach — We discuss traditional approaches to effectiveness evaluation based on test collections, then transition to approaches based on test collections along with explicit models of user interaction with search results. We contrast this with approaches for which the user is ‘in the loop’, such as user studies and online evaluations.

Research limitations/implications — If it were possible to model users perfectly, we could directly estimate the utility of a search engine to its users; this would undoubtedly have a transformative effect on information retrieval and web search research. In practice, this goal will never be achievable because users exhibit far too much variability in how they approach the search engine, and furthermore provide valuable feedback that models and simulations cannot provide. Nevertheless, better models of user interaction will help develop better web search engines for a wider variety of tasks more rapidly.

Originality/value of paper — This is the first work that surveys recent work on user model-based evaluation and places it in a context with traditional evaluation based on the Cranfield paradigm.

The use of technology in Saudi Arabian higher education is constantly evolving. With the support of the 2030 Saudi vision, many research studies have started covering learning analytics and Big Data in the Saudi Arabian higher education. Examining learning analytics in higher education institutions promise transforming the learning experience to maximize students' learning potential. With the thousands of students' transactions recorded in various learning management systems (LMS) in Saudi educational institutions, the need to explore and research learning analytics in Saudi Arabia has caught the interest of scholars and researchers regionally and internationally. This chapter explores a Saudi private university in Jeddah, Saudi Arabia, and examines its rich learning analytics and discovers the knowledge behind it. More than 300,000 records of LMS analytical data were collected from a consecutive 4-year historic data. Romero, Ventura, and Garcia (2008) educational data mining process was applied to collect and analyze the analytical reports. Statistical and trend analysis were applied to examine and interpret the collected data. The study has also collected lecturers' testimonies to support the collected analytical data. The study revealed a transformative pedagogy that impact course instructional design and students' engagement.

This study investigates the relationship among economic growth, energy usage, urbanization, trade openness, carbon emission, ecological footprint, and ecological carbon footprint with the sample of G-7 countries over the period 1991–2017. The Continuously-Updated and Fully-Modified (CUP-FM) estimator method applied to the panel series is utilized. As a result of empirical analysis, an increase in the real per capita GDP increases the ecological footprint by 0.844%. Also, there are positive impacts that are detrimental to the environment among the environmental quality and explanatory variables for each established model. The findings indicate that a relationship exists among the variables in the long run, and at the same time, carbon emission as an indicator of pollution should be noticed as well as the ecological footprint and ecological carbon footprint. It is observed that a positive relationship exists among the variables such as economic growth, energy usage, and environmental pollution indicator. In this context, G-7 countries indicate that, upon taking their developed economic structures and global pollution levels into account, the Environment Kuznets curve (EKC) hypothesis becomes valid, and that the U-shaped behavior is valid for the total ecological footprint. It is been seen that, besides economic growth and energy usage, other explanatory variables contribute to environmental degradation in trade openness. In compliance with these results, in terms of the sustainability of economic development, G-7 countries should pay attention to economic activities that would impair the environmental quality and control them with regulations.

The potential advantage of extreme value theory in modeling management phenomena is the central theme of this paper. The statistics of extremes have played only a very limited role in management studies despite the disproportionate emphasis on unusual events in the world of managers. An overview of this theory and related statistical models is presented, and illustrative empirical examples provided.

We examine the effect of firm ownership status on three environmentally relevant variables: energy efficiency, toxic emissions, and spending on pollution abatement. Prior research has demonstrated that public firms invest less than private firms and suggests this difference is due pressure from investors to strongly favor short over long-term earnings. We extend this logic to other firm behavior, examining whether publicly owned facilities invest in energy efficiency and pollution reduction differently than privately owned facilities. Using data from the US Census of Manufactures from 1980 to 2009, information on pollution from the Environmental Protection Agency Toxic Release Inventory (TRI) and pollution abatement spending from the Pollution Abatement Costs and Expenditures survey, we find that facilities switching to public ownership are less energy efficient and spend less on pollution abatement than their privately owned counterparts. However, we also find that facilities switching to public ownership have lower toxic emissions than other facilities. We also examine how different sources of external pressures alter these results and find that increased regulatory scrutiny is correlated with increased energy efficiency, toxic emissions, and abatement spending. More concentrated institutional ownership in public firms is associated with lower energy efficiency as is a greater brand focus. These latter results are broadly consistent with the idea that publicly owned firms respond to pressures from investors with a reduced focus on environmentally relevant variables. However, since facilities switching to public ownership have lower toxic emissions, this suggests that there are two competing pressures in publicly owned facilities: cost pressures, consistent with lowered energy efficiency, and public perceptions, consistent with lower toxic emissions, particularly since TRI data became available. In this sense, the combination of ownership and transparency of information appears to influence how firms prioritize different stakeholders.

Academic social networking (ASN) sites are becoming a popular communication medium among scholars. This case study was designed to explore communication behaviors of physicists, linguists, and sociologists on an ASN site called Academia.edu, their motivations for using it, and the perceived impact of their use of the site on their professional activities. Results from this study are valuable for designing computer-mediated and web-based communication media for scholars and also for adding richness to the literature related to scholarly communication. For the purpose of this study, data was collected using three different instruments: Server log, survey, and interview. Data used for analyses included a total of 20,309 server log data, 267 survey responses, and 28 interviews from scholars of Physics, Sociology, and Linguistics who use Academia.edu. Results from the study showed that the use of Academia.edu is dependent on the discipline scholars are affiliated with, their professional status, and the time of the year. Unlike physicists, linguists and sociologists are more inclined to using Academia.edu and other ASN sites. Although linguists and sociologists actively use Academia.edu, their motivations to use the site are different. These differences in user-motivations and user-activities across the disciplines are influenced by variations in the social and cultural practices of the disciplines. This study used Whitley’s (2000) theory of degrees of mutual dependence and task uncertainty to explain the differences in the adoption and use of Academia.edu across the three disciplines.

In this chapter we develop and implement a method for maximum simulated likelihood estimation of the continuous time stochastic volatility model with the constant elasticity of volatility. The approach does not require observations on option prices, nor volatility. To integrate out latent volatility from the joint density of return and volatility, a modified efficient importance sampling technique is used after the continuous time model is approximated using the Euler–Maruyama scheme. The Monte Carlo studies show that the method works well and the empirical applications illustrate usefulness of the method. Empirical results provide strong evidence against the Heston model.

For central banks who study the use of cash, acceptance of card payments is an important factor. Surveys to measure levels of card acceptance and the costs of payments can be complicated and expensive. In this paper, we exploit a novel data set from Hungary to see the effect of stratified random sampling on estimates of payment card acceptance and usage. Using the Online Cashier Registry, a database linking the universe of merchant cash registers in Hungary, we create merchant and transaction level data sets. We compare county (geographic), industry and store size stratifications to simulate the usual stratification criteria for merchant surveys and see the effect on estimates of card acceptance for different sample sizes. Further, we estimate logistic regression models of card acceptance/usage to see how stratification biases estimates of key determinants of card acceptance/usage.