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This study investigates the validity of Ferreira et al.’s (2020) Organizational Bullshit Perception Scale by examining its distinctiveness from similar constructs (perceptions of organizational politics, organizational cynicism, procedural justice) and its predictive validity through its relations with important organizational attitudes (organizational identification) and behaviors (counterproductive work behavior and organizational citizenship behavior). This study also examines the moderating effects of honesty–humility on the relations between organizational bullshit perception and the outcomes of counterproductive work behavior, organizational citizenship behavior and organizational identification. Finally, this study examines the incremental validity of organizational bullshit perception in predicting counterproductive work behavior, organizational citizenship behavior and organizational identification above and beyond similar constructs in an exploratory fashion.

Survey data were collected from a sample of working adults online via Amazon’s Mechanical Turk platform across two waves (final N = 323 for wave 1 and 174 for wave 2), one month apart.

The results indicate that organizational bullshit perception, as measured by Ferreira et al.’s (2020) scale, represents a distinct construct that has statistically significant relations with counterproductive work behavior, organizational citizenship behavior and organizational identification, even after controlling for procedural justice, organizational cynicism and perceptions of organizational politics. The results, however, showed no support for honesty–humility as a moderator.

These findings suggest that organizations can benefit from assessing and working to alleviate their employees’ perceptions of organizational bullshit. This construct predicts behaviors and attitudes important for organizational functioning.

This study adds to Ferreira et al.’s (2020) original work by demonstrating organizational bullshit perception’s distinctiveness from existing constructs in the literature and its implications for organizations and their employees.

This systematic literature review investigates the contribution of design thinking (DT) as a process and tool to drive innovation in a sustainable built environment (SBE) and develops a new model for sustainability research integrating DT and future thinking approaches toward achieving a unified DT and foresight notion for future research and applications.

This review was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Open-access English articles published between 2000 and 2022 identified using the EBSCOhost, Emerald Insight, DOJA, JSTOR, Scopus and Taylor and Francis database searches were reviewed. The review framework deploys a previously proposed modified Ansoff matrix with an integrated innovation matrix to identify and analyze the challenges and opportunities for innovation growth in SBE. Additionally, a citation analysis was conducted to explore the impact of DT for innovation in SBE, and a proposed framework based on design by drawing on foresight theory was developed.

Research on DT for innovation in SBE faces the challenge of unanticipated impacts. According to the average number of citations per document, innovation associated with new solutions within a new context seems to become highly influential. Additionally, research gaps exist in the integration of foresight and DT into sustainability research to identify new contexts and solutions to SBE. A model of foresight design thinking (FDT) is proposed to guide future research and support the practical application of DT in sustainability.

This analysis was limited by the selection criteria as only certain keywords were used and English-only articles were selected. Future research should consider the use of DT for innovation in SBE using various important keywords, which would improve research findings and expand the contribution of DT to SBE.

The FDT model offers a new holistic framework for the iterative process of reframing and reperception, focusing on divergent and convergent thinking with the goal of contributing to SBE practices.

The integrated framework of DT and foresight can contribute to the study and development of sustainable innovation and a strategic shift toward a sustainable society.

The integration of DT, foresight and sustainability can broaden the horizons of sustainability research by systematically addressing future challenges related to SBE, which can be translated into feasible and innovative solutions. Thus, the FDT model complements the application of DT in sustainable innovation in this research field.

This study primarily aims at assessing Faculty Competences Development Components (FCDCs) that help in the development of educational leaders. The study further formulates a model using seven latent constructs that explain the development of the mechanism of development of educational leaders and extend the benefits of their development to different stakeholders including faculty, educational institutions and society at large.

A systematic review of the literature was conducted to identify various FCDCs to construct a conceptual framework. Later, this conceptual framework was tested through empirical research using the structural equation modeling (SEM) technique carried out with respect to technical institutions of Punjab (India).

The study identified that organizational roles are of the highest importance for the competences development of educational leaders followed by teachers’ attributes and teachers’ roles. The study’s findings also revealed that FCDCs significantly impact beneficiaries by developing competent educational leaders as mediators between the FCDCs and beneficiaries link. The biggest beneficiaries of development are faculty members of these institutes in terms of their performance enhancements.

The study is unique in terms of developing a model for the competences development of educational leaders and helpful in understanding various benefits of the educational leaders to various educational stakeholders.

Arts-integration is a promising approach to building students’ abilities to create and critique arguments with data, also known as informal inferential reasoning (IIR). However, differences in disciplinary practices and routines, as well as school organization and culture, can pose barriers to subject integration. The purpose of this study is to describe synergies and tensions between data science and the arts, and how these can create or constrain opportunities for learners to engage in IIR.

The authors co-designed and implemented four arts-integrated data literacy units with 10 teachers of arts and mathematics in middle school classrooms from four different schools in the USA. The data include student-generated artwork and their written rationales, and interviews with teachers and students. Through maximum variation sampling, the authors identified examples from the data to illustrate disciplinary synergies and tensions that appeared to support different IIR processes among students.

Aspects of artistic representation, including embodiment, narrative and visual image; and aspects of the culture of arts, including an emphasis on personal experience, the acknowledgement of subjectivity and considerations for the audience’s perspective, created synergies and tensions that both offered and hindered opportunities for IIR (i.e. going beyond data, using data as evidence and expressing uncertainty).

This study answers calls for humanistic approaches to data literacy education. It contributes an interdisciplinary perspective on data literacy that complements other context-oriented perspectives on data science. This study also offers recommendations for how designers and educators can capitalize on synergies and mitigate tensions between domains to promote successful IIR in arts-integrated data literacy education.

With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students.

Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers’ responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments.

Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being “messy,” as requiring more work for the student or analyst to pore through than other data sets and as involving computation.

Analysis of teachers’ aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.