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1 – 10 of 20Peter M. Kruyen, Shelena Keulemans, Rick T. Borst and Jan-Kees Helderman
Since the early 1980s, western governments are assumed to have been either moving toward post-bureaucratic models or transforming into so-called neo-Weberian bureaucracies. As…
Abstract
Purpose
Since the early 1980s, western governments are assumed to have been either moving toward post-bureaucratic models or transforming into so-called neo-Weberian bureaucracies. As different public-sector (reform) models imply different ideal typical personality traits for civil servants, the purpose of this paper is to ask the question to what extent personality requirements that governments demand from their employees have evolved over time in line with these models.
Design/methodology/approach
The authors analyzed the use of big-five traits in a sample of 21,003 job advertisements for local government jobs published between 1980 and 2017, applying tools for computer-assisted text analysis.
Findings
Using multilevel regression analyses, the authors conclude that, over time, there is a significant increase in the use of personality descriptors related to all big-five factors.
Research limitations/implications
The authors postulate that governments nowadays are actively looking for the “renaissance bureaucrat” in line with the neo-Weberian bureaucracy paradigm. The authors end with a discussion of both positive and negative consequences of this development.
Originality/value
First, the authors explicitly link personality, public administration, and public management using the Abridged Big-Five-Dimensional Circumflex model of personality. Second, by linking observed trends in civil servant personality requirements to larger theories of public-sector reform models, the authors narrow the gap between public administration theories and practice. Third, the software tools that the authors use to digitalize and analyze a large number of documents (the job ads) are new to the discipline of public administration. The research can therefore serve as a guideline for scholars who want to use software tools to study large amounts of unstructured, qualitative data.
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Beatriz Picazo Rodríguez, Antonio Jose Verdú-Jover, Marina Estrada-Cruz and Jose Maria Gomez-Gras
To understand how organizations, public or private, must increase their productivity perception (PP), independently of the sector. This article aims to analyze PP in the digital…
Abstract
Purpose
To understand how organizations, public or private, must increase their productivity perception (PP), independently of the sector. This article aims to analyze PP in the digital transformation (DT) process to determine how it is affected by technostress (TS) and work engagement (WE), two concepts that seem to be forces opposing PP.
Design/methodology/approach
The authors use data from a questionnaire addressed to personnel in two organizations (public and private). The analysis applies partial least squares technique to the 505 valid responses obtained from these organizations. This analysis is based not on representativeness but on uniqueness.
Findings
The results suggest a positive, significant relationship between DT and PP. This article integrates DT and its effects on aspects of people's health, PP and WE. The model thus includes interactions of technology with human elements. In both business and administrative environments, PP is key to optimizing resources and survival of organizations.
Research limitations/implications
DT processes are different and complex because every organization is different. The authors recommend expanding this study to other sectors in both spheres, public and private. Aligning the objectives of the institutions for aid with DT is also quite complicated.
Practical implications
This study contributes to improving participating organizations. It also provides government institutions with a clear foundation from which to encourage actions that promote the health and WE of their workforce without reducing productivity. In addition, this study adds novelty to the research line.
Originality/value
The authors have deepened this line of research by developing fuller knowledge of the relationships among novel and necessary variables in organizations. The authors provide complementary, different and inspiring value in addressing this line of research.
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Julian Hocker, Christoph Schindler and Marc Rittberger
The open science movement calls for transparent and retraceable research processes. While infrastructures to support these practices in qualitative research are lacking, the…
Abstract
Purpose
The open science movement calls for transparent and retraceable research processes. While infrastructures to support these practices in qualitative research are lacking, the design needs to consider different approaches and workflows. The paper bases on the definition of ontologies as shared conceptualizations of knowledge (Borst, 1999). The authors argue that participatory design is a good way to create these shared conceptualizations by giving domain experts and future users a voice in the design process via interviews, workshops and observations.
Design/methodology/approach
This paper presents a novel approach for creating ontologies in the field of open science using participatory design. As a case study the creation of an ontology for qualitative coding schemas is presented. Coding schemas are an important result of qualitative research, and reuse can yield great potential for open science making qualitative research more transparent, enhance sharing of coding schemas and teaching of qualitative methods. The participatory design process consisted of three parts: a requirement analysis using interviews and an observation, a design phase accompanied by interviews and an evaluation phase based on user tests as well as interviews.
Findings
The research showed several positive outcomes due to participatory design: higher commitment of users, mutual learning, high quality feedback and better quality of the ontology. However, there are two obstacles in this approach: First, contradictive answers by the interviewees, which needs to be balanced; second, this approach takes more time due to interview planning and analysis.
Practical implications
The implication of the paper is in the long run to decentralize the design of open science infrastructures and to involve parties affected on several levels.
Originality/value
In ontology design, several methods exist by using user-centered design or participatory design doing workshops. In this paper, the authors outline the potentials for participatory design using mainly interviews in creating an ontology for open science. The authors focus on close contact to researchers in order to build the ontology upon the expert's knowledge.
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This chapter reflects on the understanding of the phenomenon known as Smart Industry, Industry 4.0, fourth industrial revolution, and many other labels. It does so by reflecting…
Abstract
This chapter reflects on the understanding of the phenomenon known as Smart Industry, Industry 4.0, fourth industrial revolution, and many other labels. It does so by reflecting on the issue of terminology, as well as the existing diversity regarding the description of the phenomenon. The issue of meaning is addressed by assessing the results from Culot, Nassimbeni, Orzes, and Sartor (2020) and Habraken and Bondarouk (2019) which are, subsequently, used to develop a workable description. Findings from the two assessed studies raise the question of whether a workable construction of the phenomenon is to be understood as the key technologies or the distinctive developments? A question without a definitive answer, but I will present my view by taking inspiration from the manner in which the prior industrial revolutions are commonly understood. This leads to a, still multifaceted though, more focused understanding of the phenomenon. The insights, formulated proposition and developed model stemming from the reflection of terminology and meaning of the phenomenon helps move the current technology-related phenomenon forward. They assist with the establishment of well-documented papers. A critical aspect if we aim to understand how management will look like in the era of this phenomenon.
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Sascha Friesike, Leonhard Dobusch and Maximilian Heimstädt
Many early-career researchers (ECR) are motivated by the prospect of creating knowledge that is useful, not just within but also beyond the academic community. Although research…
Abstract
Many early-career researchers (ECR) are motivated by the prospect of creating knowledge that is useful, not just within but also beyond the academic community. Although research facilities, funders and academic journals praise this eagerness for societal impact, the path toward such contributions is by no means straightforward. In this essay, we address five common concerns faced by ECRs when they strive for societal impact. We discuss the opportunity costs associated with impact work, the fuzziness of current impact measurement, the challenge of incremental results, the actionability of research findings, and the risk of saying something wrong in public. We reflect on these concerns in light of our own experience with impact work and conclude by suggesting a “post-heroic” perspective on impact, whereby seemingly mundane activities are linked in a meaningful way.
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Roy Cerqueti, Caterina Lucarelli, Nicoletta Marinelli and Alessandra Micozzi
This paper aims to dismantle the idea that sex per se explains entrepreneurial outcomes and demonstrates the influence of a gendered motivation on forging and shaping new venture…
Abstract
Purpose
This paper aims to dismantle the idea that sex per se explains entrepreneurial outcomes and demonstrates the influence of a gendered motivation on forging and shaping new venture teams, which is a disruptive choice affecting the future of start-ups.
Design/methodology/approach
A two-level research model is validated on data from the Panel Study of Entrepreneurial Dynamics II (PSED II), with a system of simultaneous equations. First, if team features affect the performance of new ventures is tested; then, the study investigates determinants of team features with a focus on sex and motivation of nascent entrepreneurs.
Findings
Human capital (HC) in terms of education and experience of team members consistently explains venture evolution only when considering the larger team of affiliates. The HC gathered by nascent entrepreneurs is not because of the simplistic sex condition, but rather to a gendered motivation related to the inferior need of achievement of women.
Research limitations/implications
Limitations of discretionary scoring assigned to items of the PSED II survey are present, but unavoidable when processing qualitative data.
Practical implications
Women need to be (culturally) educated on how to re-balance their personal motivation towards entrepreneurship by fostering their incentives for achievement. Political and educational programmes could trigger success in the creation of new businesses led by women.
Originality/value
This paper contributes to the literature on nascent entrepreneurship, focusing on the entrepreneurial teams in the initial phase of business creation, and provides the basis for further studies aimed at eradicating the stereotypes of gender roles that lead women to self-exclusion and organizational errors.
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Jennifer Ball and Muna Kashoob
Most teachers in the Gulf would agree that Arab learners struggle more with reading and writing than listening and speaking. One little considered possible influence on this is…
Abstract
Most teachers in the Gulf would agree that Arab learners struggle more with reading and writing than listening and speaking. One little considered possible influence on this is the particular visual processing requirements of English. This article suggests why visual processing or visual cognition might be a particular difficulty for Arab students reading English. It offers a simple classroom checklist that may assist teachers to notice if visual processing strain could be effecting their student’s attention, motivation and performance.
In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property…
Abstract
Purpose
In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.
Design/methodology/approach
All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.
Findings
Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.
Practical implications
AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.
Originality/value
According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.
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This article revisits some theories and concepts of public administration, including those related to public value, transaction costs and social equity, to analyze the advantages…
Abstract
Purpose
This article revisits some theories and concepts of public administration, including those related to public value, transaction costs and social equity, to analyze the advantages and disadvantages of using artificial intelligence (AI) algorithms in public service delivery. The author seeks to mobilize theory to guide AI-era public management practitioners and researchers.
Design/methodology/approach
The author uses an existing task classification model to mobilize and juxtapose public management theories against artificial intelligence potential impacts in public service delivery. Theories of social equity and transaction costs as well as some concepts such as red tape, efficiency and economy are used to argue that the discipline of public administration provides a foundation to ensure algorithms are used in a way that improves service delivery.
Findings
After presenting literature on the challenges and promises of using AI in public service, the study shows that while the adoption of algorithms in public service has benefits, some serious challenges still exist when looked at under the lenses of theory. Additionally, the author mobilizes the public administration concepts of agenda setting and coproduction and finds that designing AI-enabled public services should be centered on citizens who are not mere customers. As an implication for public management practice, this study shows that bringing citizens to the forefront of designing and implementing AI-delivered services is key to reducing the reproduction of social biases.
Research limitations/implications
As a fast-growing subject, artificial intelligence research in public management is yet to empirically test some of the theories that the study presented.
Practical implications
The paper vulgarizes some theories of public administration which practitioners can consider in the design and implementation of AI-enabled public services. Additionally, the study shows practitioners that bringing citizens to the forefront of designing and implementing AI-delivered services is key to reducing the reproduction of social biases.
Social implications
The paper informs a broad audience who might not be familiar with public administration theories and how those theories can be taken into consideration when adopting AI systems in service delivery.
Originality/value
This research is original, as, to the best of the author’s knowledge, no prior work has combined these concepts in analyzing AI in the public sector.
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