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Branched situational judgment tests (BSJTs) are an increasingly common employee selection method, yet there is no theory and very little empirical work explaining the designs and impacts of branching. To encourage additional research on BSJTs, and to provide practitioners with a common language to describe their current and future practices, we sought to develop a theory of BSTJs.

Given the absence of theory on branching, we utilized a ground theory qualitative research design, conducting interviews with 25 BSJT practitioner subject matter experts.

Our final theory consists of three components: (1) a taxonomy of BSJT branching features (contingency, parallelism, convergence, and looping) and options within those features (which vary), (2) a causal theoretical model describing impacts of branching in general on applicant reactions via proximal effects on face validity, and (3) a causal theoretical model describing impacts on applicant reactions among branching designs via proximal effects on consistency of administration and opportunity to perform.

Our work provides the first theoretical foundation on which future confirmatory research in the BSJT domain can be built. It also gives both researchers and practitioners a common language for describing branching features and their options. Finally, it reveals BSJTs as the results of a complex set of interrelated design features, discouraging the oversimplified contrasting of “branching” vs “not branching.”

Assessment gamification, which refers to the addition of game elements to existing assessments, is commonly implemented to improved applicant reactions to existing psychometric measures. This study aims to understand the effects of gamification on applicant reactions to and measurement quality of situational judgment tests.

In a 2 × 4 between-subjects experiment, this study randomly assigned 315 people to experience different versions of a gamified situational judgment test, crossing immersive game elements (text, audio, still pictures, video) with control game elements (high and low), measuring applicant reactions and assessing differences in convergent validity between conditions.

The use of immersive game elements improved perceptions of organizational technological sophistication, but no other reactions outcomes (test attitudes, procedural justice, organizational attractiveness). Convergent validity with cognitive ability was not affected by gamification.

This is the first study to experimentally examine applicant reactions and measurement quality to SJTs based upon the implementation of specific game elements. It demonstrates that small-scale efforts to gamify assessments are likely to lead to only small-scale gains. However, it also demonstrates that such modifications can be done without harming the measurement qualities of the test, making gamification a potentially useful marketing tool for assessment specialists. Thus, this study concludes that utility should be considered carefully and explicitly for any attempt to gamify assessment.

The purpose of this paper is to determine the impact of corporate investments in corporate social responsibility (CSR), measured by the environmental, social and government (ESG) rating, on the market valuation of a firm's stocks and to explain the regional differences in the degree of this influence.

The empirical study uses linear and non-linear panel regression models for a panel sample of 951 firms listed in Asia, North America and Europe operating in innovative industries.

The CSR score was found to be significant in terms of stock excess return on the regional level. However, this finding cannot be extrapolated to the global scale. ESG rating is priced by the European and North American markets negatively, while in the Asian market, it is positive. This penalty (negative influence) is greater than the reward for one point increase in ESG rating.

The results of this empirical study could be used by firms' managers to adjust strategies aimed at stock value growth and by investors to select an investment strategy to maximize return.

The impact of investments in CSR on stock excess return over a defined benchmark is assessed. The study reveals regional differences in the impact of CSR investment using a sample of Asian, European and North American firms. The authors apply a more advanced lagged CSR performance (d.ESG) assessment based on the methodology of Zhang and Rajagopalan (2010).

Federal regulatory agencies are created by Congress to mitigate particular social problems, such as pollution (the Environmental Protection Agency), discrimination (the Equal Employment Opportunity Commission), and anticompetitive conduct (the Federal Trade Commission). These agencies have the delegated authority to issue Rules and Regulations that have the force of law within their respective domains, constrained by the oversight of the President and Congress, and by litigation through the Courts. Many view the extent of such oversight as inefficiently lax, with the result that “missionary” bureaucracies successfully overregulate and inefficiently extend the span of their authority. After describing these concerns, I develop a model of agency bias that extends my earlier work with Canice Prendergast and Topel (1993, 1996) to a regulatory framework. In the model, activist bureaucrats who seek greater regulation are attracted to an agency's mission. Their biases are constrained by the courts, where agency rules and regulations can be challenged, and by oversight from other branches of government. In equilibrium, agencies gain from the exercise of bias even though all parties know it occurs and seek to mitigate its costs. The public sector is overregulated on average. Overregulation is largest when the social problem is least harmful, and when oversight of agency actions is weak. Stronger oversight would reduce the distortionary effect of agency biases. More precise legislative language would provide clearer guidance to the court system, which would reduce deference to biased agency opinions in the formation of regulations.

This paper reports on a quantitative study of data gathered from the Linked Open Vocabularies (LOV) catalogue, including the use of network analysis and metrics. The purpose of this paper is to gain insights into the structure of LOV and the use of vocabularies in the Web of Data. It is important to note that not all the vocabularies in it are registered in LOV. Given the de-centralised and collaborative nature of the use and adoption of these vocabularies, the results of the study can be used to identify emergent important vocabularies that are shaping the Web of Data.

The methodology is based on an analytical approach to a data set that captures a complete snapshot of the LOV catalogue dated April 2014. An initial analysis of the data is presented in order to obtain insights into the characteristics of the vocabularies found in LOV. This is followed by an analysis of the use of Vocabulary of a Friend properties that describe relations among vocabularies. Finally, the study is complemented with an analysis of the usage of the different vocabularies, and concludes by proposing a number of metrics.

The most relevant insight is that unsurprisingly the vocabularies with more presence are those used to model Semantic Web data, such as Resource Description Framework, RDF Schema and OWL, as well as broadly used standards as Simple Knowledge Organization System, DCTERMS and DCE. It was also discovered that the most used language is English and the vocabularies are not considered to be highly specialised in a field. Also, there is not a dominant scope of the vocabularies. Regarding the structural analysis, it is concluded that LOV is a heterogeneous network.

The paper provides an empirical analysis of the structure of LOV and the relations between its vocabularies, together with some metrics that may be of help to determine the important vocabularies from a practical perspective. The results are of interest for a better understanding of the evolution and dynamics of the Web of Data, and for applications that attempt to retrieve data in the Linked Data Cloud. These applications can benefit from the insights into the important vocabularies to be supported and the value added when mapping between and using the vocabularies.

This paper aims to explore the country-of-origin effect, specifically its potential impact on beer labeling, from a linguistic perspective.

The paper opted for an exploratory study using Sebba’s framework for multilingual texts (2012). Briefly, analysis developed through the observation, the use of notes taken during the phase of data collection and their comparison.

The paper provides empirical insights on how beer labels appear to signal some interesting occurring trends. First, this investigation seems to suggest a link between languages used and their potential to recall country images that producers may be willing to stimulate and enhance. Second, data appeal to products’ countries of origin, using official languages, texts and visual elements strictly interrelated with local cultures.

Because of the chosen approach, results may lack generalizability. Therefore, researchers are encouraged to apply this framework or explore the same phenomena in other product categories and geographical markets too. Finally, deeper insights on the topic could be reached taking into consideration other financial data, for example market performance.

The paper includes implications for the development of further research regarding brand image and reputation, in general, and the country-of-origin effect, specifically.

This project is innovative for two main reasons: first, its methodological approach and, second, its combination of linguistics and marketing-related aspects. Hence, exploring possible links across the two disciplines, ultimately trying to examine potential reasons underlying their use, was the final objective of this paper. Finally, no existing publications appear to use Sebba’s framework to analyze beer labels from a linguistic perspective. Consequently, no researchers seem to have explored potential interrelations among this analysis and marketing concepts and strategies.

Ensuring quality is one of the most significant challenges in microtask crowdsourcing tasks. Aggregation of the collected data from the crowd is one of the important steps to infer the correct answer, but the existing study seems to be limited to the single-step task. This study aims to look at multiple-step classification tasks and understand aggregation in such cases; hence, it is useful for assessing the classification quality.

The authors present a model to capture the information of the workflow, questions and answers for both single- and multiple-question classification tasks. They propose an adapted approach on top of the classic approach so that the model can handle tasks with several multiple-choice questions in general instead of a specific domain or any specific hierarchical classifications. They evaluate their approach with three representative tasks from existing citizen science projects in which they have the gold standard created by experts.

The results show that the approach can provide significant improvements to the overall classification accuracy. The authors’ analysis also demonstrates that all algorithms can achieve higher accuracy for the volunteer- versus paid-generated data sets for the same task. Furthermore, the authors observed interesting patterns in the relationship between the performance of different algorithms and workflow-specific factors including the number of steps and the number of available options in each step.

Due to the nature of crowdsourcing, aggregating the collected data is an important process to understand the quality of crowdsourcing results. Different inference algorithms have been studied for simple microtasks consisting of single questions with two or more answers. However, as classification tasks typically contain many questions, the proposed method can be applied to a wide range of tasks including both single- and multiple-question classification tasks.

The purpose of this paper is to review the current status of research on Wikidata and, in particular, of articles that either describe applications of Wikidata or provide empirical evidence, in order to uncover the topics of interest, the fields that are benefiting from its applications and which researchers and institutions are leading the work.

A systematic literature review is conducted to identify and review how Wikidata is being dealt with in academic research articles and the applications that are proposed. A rigorous and systematic process is implemented, aiming not only to summarize existing studies and research on the topic, but also to include an element of analytical criticism and a perspective on gaps and future research.

Despite Wikidata’s potential and the notable rise in research activity, the field is still in the early stages of study. Most research is published in conferences, highlighting such immaturity, and provides little empirical evidence of real use cases. Only a few disciplines currently benefit from Wikidata’s applications and do so with a significant gap between research and practice. Studies are dominated by European researchers, mirroring Wikidata’s content distribution and limiting its Worldwide applications.

The results collect and summarize existing Wikidata research articles published in the major international journals and conferences, delivering a meticulous summary of all the available empirical research on the topic which is representative of the state of the art at this time, complemented by a discussion of identified gaps and future work.

This study seeks to explore the demand side of the labour market influenced by the digital revolution. It aims at identifying the new composition of skills and their value as implicitly manifested by employers when they look for the new labour force. The authors analyse the returns to computing skills based on text mining techniques applied to the job advertisements.

The methodology is based on the hedonic pricing model with the Heckman correction to overcome the sample selection bias. The empirical part is based on a large data set that includes more than 9m online vacancies on one of the biggest job boards in Russia from 2006 to 2018.

Empirical evidence for both negative and positive returns to computing skills and their monetary values is found. Importantly, the authors also have found both complementary and substitutional effects within and between non-domain (basic) and domain (advanced) subgroups of computing skills.

Apart from the empirical evidence on the value of professional computing skills and their interrelations, this study provides the important methodological contribution on applying the hedonic procedure and text mining to the field of human resource management and labour market research.

Data providers have been increasingly publishing content as linked data (LD) on the Web. This process includes guidelines (i.e. good practices) to publish, share, and connect data on the Web. Several people in different areas, for instance, sciences, medicine, governments and so on, use these practices to publish data. The LD community has been proposing many practices to aid the publication of data on the Web. However, discovering these practices is a costly and time-consuming task, considering the practices that are produced by the literature. Moreover, the community still lacks a comprehensive understanding of how these practices are used for publishing LD. Thus, the purpose of this paper is to investigate and better understand how best practices support the publication of LD as well as identifying to what extent they have been applied to this field.

The authors conducted a systematic literature review to identify the primary studies that propose best practices to address the publication of LD, following a predefined review protocol. The authors then identified the motivations for recommending best practices for publishing LD and looked for evidence of the benefits of using such practices. The authors also examined the data formats and areas addressed by the studies as well as the institutions that have been publishing LD.

In summary, the main findings of this work are: there is empirical evidence of the benefits of using best practices for publishing LD, especially for defining standard practices, integrability and uniformity of LD; most of the studies used RDF as data format; there are many areas interested in dissemination data in a connected way; and there is a great variety of institutions that have published data on the Web.

The results presented in this systematic review can be very useful to the semantic web and LD community, since it gathers pieces of evidence from the primary studies included in the review, forming a body of knowledge regarding the use best practices for publishing LD pointing out interesting opportunities for future research.