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The analysis presented in this chapter defines three different synthetic measurements of disincentives for formal work: two standard measurements, namely, the tax wedge and the marginal effective tax rate (METR); and a new, innovative measurement called formalization tax rate (FTR). The novelty of the latter is that it measures disincentives stemming not only from labor taxation but also from benefit withdrawal due to formalization. A descriptive analysis across a large number of OECD and Eastern European countries reveals that the disincentives for formal work – when measured through the FTR – are especially high for low-wage earners. This suggests that formal work might not pay in this segment of the labor market, in particular for the so-called mini-jobs and midi-jobs (low-paying part-time work).

Another novelty of the chapter is its empirical approach. Using EU-SILC 2008 data and OECD Tax and Benefit data for six Eastern European countries (Bulgaria, the Czech Republic, Estonia, Latvia, Poland, and Slovakia), we match disincentives for formal work to individual observations in a large data set. Applying a probit regression, the analysis finds a significant positive correlation between FTR or METR and the incidence of being informal. In other words, controlling for individual and job characteristics, the higher the FTR or the METR that individuals are facing is, the more likely they are to work informally. The tax wedge, on the other hand, yields a negative correlation. This indicates that the tax wedge is not sufficiently capturing disincentives for formal work. We also conclude that in cross-country analysis, it might be more useful to use the tax wedge that applies to low-wage earners as opposed to average wage earners.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

Technical solutions can be important when key communicators take on the task of making sense of social media flows during crises. However, to provide situation awareness during high-stress assignments, usability problems must be identified and corrected. In usability studies, where researchers investigate the user-friendliness of a product, several types of data gathering methods can be combined. Methods may include subjective (surveys and observations) and psychophysiological (e.g. skin conductance and eye tracking) data collection. This chapter mainly focuses on how the latter type can provide detailed clues about user-friendliness. Results from two studies are summarised. The tool tested is intended to help communicators and journalists with monitoring and handling social media content during times of crises.

Models of value creation that have been proposed for supporting value-based management are described and analyzed, including the Balanced Scorecard, the Baldrige Quality Award Criteria, the Deming Management Method, the Service-Profit Chain, and the Skandia Intellectual Capital Model. These models are compared, their potential for guiding the identification of value drivers and performance measures for value-based management is assessed, and management issues that must be addressed if such models are to contribute to long-run value creation are explored. These issues include causally linking value drivers to each other and to financial outcomes, the extent to which the models take a dynamic, or whole-system, view of value creation, and whether multiple value drivers should be explicitly weighted and combined to form a “value index.” Finally, the substantial body of research evidence linking intangible value drivers to financial outcomes is reviewed, and some directions for further research are offered.

Many corporations have annual expenditures in research and development in the range of billions of U.S. dollars. Senior managers have often been frustrated by the lack of innovation in their organizations and have been looking for better ways to implement an innovation strategy. To provide initial evidence on this significant topic, we conduct an empirical examination and contribute to the existing literature in two important areas. First, we examine how managers choose what measures to pay attention to in managing the innovation process – defined as the process of creative definition, development, and commercialization of substantially new products, services or businesses. We find that managers use measures about specific phases of the innovation process together. For example, measures that inform about the execution stage of the innovation process are grouped together rather than being grouped with measures informative about other phases of the innovation process, such as market performance. This pattern “focused” around specific phases is in contrast to the alternative “balanced” pattern where managers would use measures from various phases of the process together. This result provides the first empirical test of how managers combine measures to filter information about business processes. It also provides important new evidence on the use of measures and provides guidance to the design of measurement systems. Second, this paper provides empirical evidence on the relationship between innovation strategy and the use of measures. Though previous studies have linked innovation strategy and the use of management control systems in general, there is little empirical data on the relationship of strategy and the use of measures and on the innovation process. We find that different dimensions of strategy are positively associated with how managers use different types of measures.

The purpose of this chapter is to trace the evolution of the concept of measurement in financial reporting and to address the question of whether measurement in financial reporting is a process of “measurement” or whether it constitutes something else, which should not be called measurement, but rather characterized a practice which assigns numbers to elements in financial statements as opposed to the process of measuring. The chapter begins with an examination of the concepts of measurement put forth in recent years by the United States Financial Accounting Standards Board and the International Accounting Standards Board, followed by a summary of the general theory of measurement in the natural sciences, and finally a review of the arguments raised by accounting theorists such as Edwards and Bell (1961), Chambers (1966), and Sterling (1970) with respect to the measurement of business income. We agree with Sterling’s argument that business income should be “measured” by the difference between net equity (i.e., assets − liabilities) at two points in time, adjusted for investments and disinvestments by owners, and also the argument that the difference in equity should be determined by the change in exit prices of net assets at the beginning and the end of the accounting period. However, we are less convinced by his argument that the determination of exit prices constitutes a “measurement” process. This leads to the principle argument of this chapter, which is that “measurement” in financial accounting may not constitute a measurement process at all.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

Organization development (OD) and people analytics (PA) have developed and are typically practiced as entirely separate and nonoverlapping disciplines in organizations. We review the principles underlying each of the two disciplines and show much greater overlap and similarities than commonly believed. An integrated framework is provided, along with examples of OD tools that should be part of the PA toolkit for any practitioner. Case studies of what the integrated framework looks like when applied in practice are discussed.

Ladakh is an isolated arid environment in the Western Himalayas whose population relies mainly on glacial melt water. If the predicted adverse impacts of climate change occur, rising temperatures would accelerate the retreat of glaciers and place immense stress on the traditional Ladakhi agriculture and way of life. Very few studies in hydrology and glaciology currently document physical processes happening in Ladakh and only one project has combined climate data based upon measurements of temperature and precipitation, collected by the Indian Air Force in Leh town, and perceptions of local communities in order to explore the potential impacts of climate change in the area. This information constitutes the basis for climate change-related interventions of nongovernmental organizations (NGOs), both local and international, and could help inform any future climate modeling. However, the quality of this data can be questioned on several points, it terms of accuracy, availability and, most importantly, usefulness. Moreover, this chapter discusses the relevance of this kind of data when the focus is placed upon the adaptation of local communities to global environmental changes where climate change may not be the primary cause. For instance, the region is also currently undergoing a rapid transition from subsistence farming to a market-based economy due to the integration of Ladakh into India and the growing influx of tourism. When addressing the broader context of environmental change, reliable, accurate, and available climate data and models could be useful only if used as part of a holistic approach. This approach requires research and interventions to combine scientific information with local knowledge and perceptions about the impacts of climate change to root the physical data in a “real world” context. It must also acknowledge other drivers of environmental changes such as unsustainable development.