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The purpose of this paper is three-fold. Firstly, through selected case studies, to provide an overview of how non-traditional data from digital public services were used as a source of knowledge for policymaking. Secondly, to argue for a design for policy approach to support the successful integration of non-traditional data into policymaking practice, thus supporting data-driven innovation for policymaking. Thirdly, to encourage a vision of the relation between data-driven innovation and public policy that considers policymaking outside the authoritative instrumental logic perspective.

A qualitative small-N case study analysis based on desk research data was developed to provide an overview of how data-centric public services could become a source of knowledge for policymaking. The analysis was based on an original theoretical-conceptual framework that merges the policy cycle model and the policy capacity framework.

This paper identifies three potential areas of contribution of a design for policy approach in a scenario of data-driven innovation for policymaking practice: the development of sensemaking and prefiguring activities to shape a shared rationale behind intra-/inter-organisational data sharing and data collaboratives; the realisation of collaborative experimentations for enhancing the systemic policy analytical capacity of a governing body, e.g. by integrating non-traditional data into new and trusted indicators for policy evaluation; and service design as approach for data-centric public services that connects policy decisions to the socio-technical context in which data are collected.

The small-N sample (four cases) selected is not representative of a broader population but isolates exemplary initiatives. Moreover, the analysis was based on secondary sources, limiting the assessment quality of the real use of non-traditional data for policymaking. This level of empirical understanding is considered sufficient for an explorative analysis that supports the original perspective proposed here. Future research will need to collect primary data about the potential and dynamics of how data from data-centric public services can inform policymaking and substantiate the proposed areas of a design for policy contribution with practical experimentations and cases.

This paper proposes a convergence, yet largely underexplored, between the two emerging perspectives on innovation in policymaking: data for policy and design for policy. This convergence helps to address the designing of data-driven innovations for policymaking, while considering pragmatic indications of socially acceptable practices in this space for practitioners.

This paper aims to study the effect of family socioeconomic status (SES) on academic and labor market outcomes.

The authors used a rich data set of administrative records for test scores, individual background and adult earnings of a cohort of agents, covering a period spanning the agents' upper-secondary education and their early years in the labor market.

The authors find that students with the highest SES obtained a 1.5 standard deviations higher score in the college admission test than students who had the same academic outcomes in the eighth grade test but belong to the lowest SES. Similarly, among students that obtained the same scores in the college admission test, those with the highest SES earned monthly wages 0.7 standard deviations higher than those with the lowest SES.

The findings highlight that family socioeconomic background continues to influence outcomes during individuals’ upper secondary education and early years in the labor market.

Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers.

This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords.

Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich.

This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.

The authors develop and validate a conceptual model, the retirement engagement model (REM), to understand the relationships between behavioral engagement (retirement information search), cognitive factors and engagement (e.g. beliefs and financial knowledge), emotional engagement (e.g. anxiety), and socio-demographic factors. Approach: The authors derive the REM through a three-step procedure: (1) an extensive literature review, (2) interactive feedback sessions with experts to confirm the model's academic and managerial relevance, and (3) an empirical test of the REM with field data (N = 583). The authors use a partial least squares (PLS) structural equation model and examine heterogeneity through a finite mixture model.

Around the globe, people are insufficiently engaged with retirement planning. The customer engagement literature offers rich insights into antecedents, outcomes, and barriers to engagement. However, customer engagement literature lacks insights into cognitive, emotional and behavioral factors that drive engagement in retirement planning, a utilitarian service context, which is important for financial well-being.

Beliefs such as perceived susceptibility, severity, benefits, barriers, and self-efficacy, together with trust and retirement anxiety, explain people's search for pension information. These factors can be used to define three clear, actionable segments of consumers.

The findings advance the customer engagement and transformative service research literature by generating insights on engagement with retirement planning, a utilitarian rather than hedonic service context that is especially relevant for financial well-being. The findings inform managerial practice and emphasize the relevance of including cognitive and emotional engagement factors that trigger behavioral engagement. The REM can help to improve pension communication. For example, the results indicate that marketers should stress the benefits of, rather than the barriers to, acquiring information.

This study aims to investigate university staff relocation from multiple separate buildings to a new building with activity-based flexible offices (AFOs) at a University in Sweden. The aim was to assess staff perceptions of the physical and psychosocial work environment and whether there were any changes in these perceptions before and after the move.

A mixed-methods design was used, analyzing closed-ended survey data at two time points (T1, n = 169 and T2, n = 160) and open-ended responses (n = 180) at T2.

The main findings revealed that employees started working more from home and that there were significant decreases in perceptions of the physical and psychosocial work environment, as well as job satisfaction, after the move to the new premises.

A comprehensive analysis of existing work processes, tasks and collaborations is crucial when planning new university premises. The planning process needs to be done in close collaboration with different stakeholders with multiple perspectives.

Introduction of AFOs in an academic setting can lead to negative consequences for occupational health and efficiency.

While extant literature has advanced our understanding of senior and middle managers in corporate entrepreneurship, studies have only recently attended to the role of non-managerial employees (NMEs). These organizational members bring ideas, resources and energy to the pursuit of innovative opportunities, yet the determinants of their entrepreneurial behavior are poorly understood.

The authors performed a systematical literature review on the subject of NMEs in corporate entrepreneurship to identify gaps and recommend an agenda for future research.

The review revealed gaps regarding (1) the distance of NMEs from decisions on corporate strategic intent, (2) agentic choices made by NMEs to use their subject matter expertise for their employers' benefit, and the influences of (3) job characteristics and (4) organizational infrastructural support of entrepreneurial behavior.

The authors present a theoretical framework and directions for future research.