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The main objective in adopting the use of metrics and analytics is to use the expertise of HR professionals in human resource management regarding their understanding of the best way to recruit, select, train, design, motivate, develop, evaluate, and retain employees at an organization to help achieve its goals more effectively.

The first and foremost step to generate metrics and analytics strategies in an organization is identification of existing problems faced by them. Owing to the changing environment and global requirement, the labor measurement also changes. The main focus is on the problems faced by the organization and human resources in the working environment.

Through the use of human resources measures and workforce analytics, decision-makers will gain the ability to more effectively manage and improve human resources programs and processes. This in turn improves the effectiveness of the workforce and organizational performance.

Metrics and analytics is a better problem-solving measure in organizations, because in any situations, decisions are made after analyzing the tactical choices.

The development of effective human resource metrics and workforce analytics is likely to be seen in the future as a very important source of competitive advantage.

The use of human resource metrics and workforce analytics improves organizational effectiveness and strategic decision-making of managers that positively impact the organization’s performance as a whole.

The purpose of this paper is to concern the use of analytics as a calculative engine enabling coordination and control for the development process in a creative digital business environment.

This research employs an explorative field study approach, using interview data from professionals working with free-to-play mobile game development. Drawing on the concepts of cycles of accumulation, accounting as an engine and mediating instruments, this study examines how organisational actors using the analytics in a digital business environment participate in the data generation that accumulates knowledge about and new insights into the desired outcome.

The real-time metrics provided the means for organisational actors to continually monitor, visualise and if necessary intervene in the creative “good game” development process. Timely quantification and visualisation of user actions, collected as digital traces, enhanced the cycle of information accumulation. This new knowledge resulted in a desire for improvement and perfection, which directed the actions towards the organisational objectives.

This study furthers our understanding of the performativity of accounting as an engine and the user behavioural data trace as its “fuel” in a digital product development. It highlights the role of analytics as a “fact-generating” device, capable of transforming the raw user behavioural data, the fuel, into powerful explanations through visualisations of ideals. The real-time metrics, understood as mediating instruments, enable the generation of new insights and accumulation of knowledge guiding the further development towards the desired outcome, the “good game”.

The purpose of this study is to understand the status quo of the use of Web analytics tools by European destination management organizations (DMOs) and to provide guidelines in using these metrics for business intelligence and tourism design. In addition, the goal is to improve destination management at the city level using Web analytics data.

In this exploratory study, the authors analyze how European DMOs view Web analytics data through the lens of the “data to knowledge to results” framework. The authors analyze the use of Web analytics tools by DMOs through the theory of affordances and “data-to-knowledge framework” developed by Davenport et al., which incorporates several factors that contribute to a successful transformation of data available to an organization to knowledge, desirable results and ultimately to building an analytical capability.

The results show that European DMOs mainly use Web analytics data for website quality assurance, but that some are also using them to drive marketing programs. The study concludes by providing several suggestions for ways in which DMOs might optimize the use of Web analytics data, which will also improve the management of destinations.

Web analytics tools are used by many organizations such as DMOs to collect traffic data, to evaluate and optimize websites. However, these metrics can also be combined with other data such as bednights numbers and used for forecasting or other managerial decisions for destination management at the city level. There is a research gap in this area that focuses on using Web analytics data for business intelligence in the tourism industry and this research aims to fill this gap.

With a focus on the evolution of human resource management (HRM) quantification over 2000–2020, this study addresses the following questions: (1) What are the data sources used to quantify HRM? (2) What are the methods used to quantify HRM? (3) What are the objectives of HRM quantification? (4) What are the representations of quantification in HRM?

This study is based on an integrative synthesis of 94 published peer-reviewed empirical and non-empirical articles on the use of quantification in HRM. It uses the theoretical framework of the sociology of quantification.

The analysis shows that there have been several changes in HRM quantification over 2000–2020 in terms of data sources, methods and objectives. Meanwhile, representations of quantification have evolved relatively little; it is still considered as a tool, and this ignores the possible conflicts and subjectivity associated with the use of quantification.

This literature review addresses the use of quantification in HRM in general and is thus larger in scope than previous reviews. Notably, it brings forth new insights on possible differences between the main uses of quantification in HRM, as well as on artificial intelligence and algorithms in HRM.

In this chapter, we apply diffusion of innovation theory and the theory of management fashion to examine the diffusion trajectory of human resource (HR) analytics in a U.S. context. We focus on the role mass media plays in influencing the diffusion process and address two research questions. First, does the mass media on HR analytics make observable the positive outcomes of HR analytics and is this related to increasing HR analytics adoption over time? Second, does the mass media on HR analytics show evidence of management trendsetting rhetoric?

We analyze published popular trade, business press, and peer-reviewed academic articles over a decade using a big data discourse analytical technique, natural language processing.

We find preliminary evidence that suggests that although the media has broadcasted positive outcomes of HR analytics, adoption has tailed off. In concert with the tailing off of HR analytic adoptions, the media appears to be recasting HR analytics as solving newer problems such as managing talent. Whether this shift makes a difference has yet to be determined.

Business press appears to influence the adoption process, both by broadcasting positive outcomes and through creating management fashion trendsetting rhetoric.

To promote the use of HR analytics, academic institutions and the HR profession need to train HR professionals in the use and benefits of HR analytics.

We lay the groundwork to improve our understanding of the role media plays in influencing how new HRM practices spread across organizations. We introduce the application of an emerging big data analytic technique, natural language processing, to analyze published media on HR analytics.

Tracing audience preferences via audience analytics software has become a vital strategy for many news organizations to ensure their competitiveness in media markets. Extant research also confirms the growing presence of these tools in digital news work in recent years across many local and international news media. However, little is understood about the analytics-driven tensions emerging among journalists and media managers. This paper aims to address this gap by drawing on the labor process theory, which critically analyzes labor and workplace transformations under capitalism.

The present study employs an interview-based qualitative methodology to deeply understand the factors at the base of the emerging tensions between news workers and managers brought about by audience metrics tools.

Results show how some perceptions, activities and contextual triggers related to analytics could make relationships between workers and managers problematic. The pressures felt by some journalists stemmed from the way their media managers introduced, interpreted, communicated and applied analytics in the workplace, which were not tied to the quality and learning goals related to journalists’ aspirations. As our evidence suggests, the analytics-induced tensions among news workers were rather an outcome of managerial deficits than of systematic plans to exploit journalists.

By identifying the nature of fundamental analytics-driven tensions in newsrooms, this paper contributes to our understanding of how media managers can embrace more effective approaches toward audience analytics, workforce and organizational performance.

The purpose of this paper is to present data that begin to detail the deficiencies of log file analytics reporting methods that are commonly built into institutional repository (IR) platforms. The authors propose a new method for collecting and reporting IR item download metrics. This paper introduces a web service prototype that captures activity that current analytics methods are likely to either miss or over-report.

Data were extracted from DSpace Solr logs of an IR and were cross-referenced with Google Analytics and Google Search Console data to directly compare Citable Content Downloads recorded by each method.

This study provides evidence that log file analytics data appear to grossly over-report due to traffic from robots that are difficult to identify and screen. The study also introduces a proof-of-concept prototype that makes the research method easily accessible to IR managers who seek accurate counts of Citable Content Downloads.

The method described in this paper does not account for direct access to Citable Content Downloads that originate outside Google Search properties.

This paper proposes that IR managers adopt a new reporting framework that classifies IR page views and download activity into three categories that communicate metrics about user activity related to the research process. It also proposes that IR managers rely on a hybrid of existing Google Services to improve reporting of Citable Content Downloads and offers a prototype web service where IR managers can test results for their repositories.

To understand human resource (HR) practices outcomes on HR decision making, strategic human resource management (HRM) and organizational performance by exploring the HR data quality along with descriptive and predictive financial and non-financial metrics.

This work utilizes the grounded theory method. After the literature was reviewed, 113 HR managers of multinational and national companies in India were interviewed with a semi-structured questionnaire. The collected interview data was analyzed with NVivo 8.0 software.

It is interesting to uncover the descriptive and predictive non-financial and financial metrics of HR practices and their influence on organizational performance. It was found that HR data quality moderates the relationship between the HR practices outcome and HR metrics. This study found that HR metrics help in HR decision-making for strategic HRM and subsequently affect organizational performance.

This study has uniquely provided the descriptive and predictive non-financial and financial metrics of HR practices and their impact on HR decision making, strategic HRM and organizational performance. This study highlights the importance of data quality. This research offers insights to the HR managers, HR analysts, chief HR officers and HR practitioners to achieve organizational performance considering the various metrics of HRM. It provides key insights to the top management to understand the HR metrics' effect on strategic HRM and organizational performance.

This paper aims to contribute to the literature on human resources (HR) digitalization, specifically on HR analytics, disentangling the concept of analytics applied to HR and explaining the factors that hinder companies from moving to analytics. Therefore, the central research questions addressed in this study are: what does HR analytics encompass? What impedes the adoption of analytics in HR within organizations?

The authors performed a comprehensive literature review on analytics as applied in HR. The authors relied on two of the major multidisciplinary publication databases (i.e. Scopus and WoS). A total of 64 manuscripts from 2010 to 2019 were content analyzed.

The results reveal that there is an ongoing confusion on HR analytics conceptualization. Yet, it seems that there is an emerging consensus on what HR analytics encompasses. The authors have identified 14 different barriers for HR analytics adoption grouped into four categories, namely, data and models, software and technology, people and management. Grounding on them the authors propose a set of 14 key factors to help to successfully adopt HR Analytics in companies.

This paper brings clarity over the conceptualization of HR analytics by offering a comprehensive definition. Additionally, it facilitates business and HR leaders in making informed decisions on adopting and implementing HR analytics. Moreover, it assists HR researchers in positioning their paper more explicitly in current debates and encouraging them to develop some future avenues of research departing from some questions posed.