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This study aims to explore the human resource management (HRM) practices related to training and feedback in the app work industry, specifically in online food delivery service, and investigate the emotional and behavioral responses of gig workers.

This study adopts a qualitative approach by interviewing 19 gig workers from six food delivery firms operating in different countries.

The results show limited training and feedback opportunities are provided to app workers, although the complexity of training and delivery methods differ across platforms. To address this shortage, app workers developed response strategies relying on social interaction.

This study adds to the research on HRM practices in the gig economy by portraying the way in which training and feedback unfold in the food delivery app ecosystem and by disclosing the gig workers’ emotional and behavioral responses to it.

This study shows that the way training activities are currently designed may provide little value to the ecosystem and are likely to produce negative emotional responses in gig workers. Thus, platform providers may make use of these findings by introducing more transparent feedback and social learning opportunities.

To the best of the authors’ knowledge, this study is among the first empirical studies on online delivery gig workers addressing specific HRM practices. It reveals significant insights for training and feedback, suggesting app economy characteristics strongly affect training and feedback practices for app workers.

This paper explores the potential that block teaching offers to enhance employability in the context of large-scale classes. It suggests that block teaching, with its condensed structure, necessitates curriculum innovation, fosters participatory learning and peer-to-peer networking, and has been shown to increase student focus and enhance engagement and attainment, especially amongst diverse learners. As these are the same challenges that large-scale teaching faces, it is proposed that intensive modes of delivery could be scaled up in a way that may help to mitigate such problems as cohorts in business schools continue to increase in size.

The paper is based on secondary research and provides an overview of literature that looks at block teaching, followed by that which explores the challenges of large-scale teaching contexts. It compares and contrasts the gaps in both to suggest a way that they could be combined.

The paper provides key insights into changes in the contemporary landscape of teaching within UK business schools, which have seen increasingly large cohorts and draws out the key strengths of intensive modes of delivery, which include helping students to time manage effectively, encouraging curriculum innovation and the creation of participatory learning opportunities as well as providing closer personal relationships between students and staff. Outlining some of the well-documented issues that can arise when teaching larger cohorts, the paper suggests that scaling up blocked delivery may offer a new way help to overcome them.

Because of the chosen research approach, the research results are subject to generalisation. Therefore, researchers are encouraged to test the proposed propositions in large-scale teaching scenarios.

This paper includes implications for the development of innovative modes of teaching in the context of large cohorts, an experience that is increasingly common amongst British business schools and beyond.

This paper brings together two bodies of literature for the first time – that of intensive modes of teaching and that focuses on large-scale teaching contexts – for the first time to show how the former may help to overcome some of the key issues arising in the latter.

This study aims to examine the relationship between medication for opioid use disorder (MOUD) among pregnant individuals, referral source, mental health, political affiliation and substance use policies in all 50 states in the USA.

This study describes MOUD receipt among pregnant people with an opioid use disorder (OUD) in 2018. The authors explored sociodemographic differences in MOUD receipt, referrals and co-occurring mental health disorders. The authors included a comparison of MOUD receipt among states that have varying substance use policies and examined the impact of these policies and the political affiliation on MOUD. The authors used multilevel binary logistic regression to examine effects of individual and state-level characteristics on MOUD.

Among 8,790 pregnant admissions with OUD, the majority who received MOUD occurred in the Northeast region (71.52%), and 14.99% were referred by the criminal justice system (n = 1,318). Of those who were self-referred, 66.39% received MOUD, while only 30.8% of referrals from the criminal justice system received MOUD. Those referred from the criminal justice system or who had a co-occurring mental health disorder were least likely to receive MOUD. The multilevel model showed that while policies were not a significant predictor, a state’s political affiliation was a significant predictor of MOUD.

The study has some methodological limitations; a state-level analysis, even when considering the individual factors, may not provide sufficient description of community-level or other social factors that may influence MOUD receipt. This study adds to the growing literature on the ineffectiveness of prenatal substance use policies designed specifically to increase the use of MOUD. If such policies are consistently assessed as not contributing to substantial increase in MOUD among pregnant women over time, it is imperative to investigate potential mechanisms in these policies that may not facilitate MOUD access the way they are intended to.

Findings from this study aid in understanding the impact that a political affiliation may have on treatment access; states that leaned more Democratic were more likely to have higher rates of MOUD, and this finding can lead to research that focuses on how and why this contributes to greater treatment utilization. This study provides estimates of underutilization at a state level and the mechanisms that act as barriers, which is a stronger assessment of how state-specific policies and practices are performing in addressing prenatal substance use and a necessary step in implementing changes that can improve the links between pregnant women and MOUD.

To the best of the authors’ knowledge, this is the first study to explore individual-level factors that include mental health and referral sources to treatment that lead to MOUD use in the context of state-level policy and political environments. Most studies estimate national-level rates of treatment use only, which can be useful, but what is necessary is to understand what mechanisms are at work that vary by state. This study also found that while substance use policies were designed to increase MOUD for pregnant women, this was not as prominent a predictor as other factors, like mental health, being referred from the criminal justice system, and living in a state with more Democratic-leaning affiliations.

Computer-assisted language learning (CALL) can create participatory cultures by removing barriers to access materials, encouraging student modes of expression, differentiating student interactions through digital environments and increasing learner autonomy. Participatory cultures require competencies or new media literacy (NML) skills to be successful in a digital world. However, professional development (PD) often lacks training on CALL and its implementation to develop such skills. The purpose of this study is to describe teachers use of digital tools for multilingual learners through a relevant theoretical perspective.

This design-based research study examines 30 in-service teachers in South Carolina, a destination state for Latinx immigrants, focusing data over three semesters of PD: interviews and instructional logs. The researchers address the question: How are teachers using digital tools to advance NML for multilingual learners (MLs)?

The authors analyzed current elementary teachers’ use of digital tools for language learning and NML purposes. Three themes are discussed: NMLs and digital literacy boundaries, digital tools for MLs and literacy teaching for MLs and NML skills.

Teacher PD often needs more specificity regarding the intersection of MLs and digital literacy. The authors contribute to the literature on needed elementary teaching practices for MLs, the integration of NML and how these practices may be addressed through PD.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

Measuring shared value (SV) created in innovation ecosystems (IEs) is increasingly relevant but complex, given the multidimensional and multiactor nature of both concepts, which challenges traditional performance measurement systems (PMSs). Moving from this gap, the authors propose an integrated approach to extend the balanced scorecard (BSC) for measuring and monitoring SV creation at IE level.

The proposed approach combines the most recent contributions on PMS in IEs and SV to define perspectives and dimensions that are better suited to deal with the nature of both IEs and SV. The approach is also applied to the real case (Alpha) of an Italian IE through a step wise method. Starting from the IE vision, the authors identify in the strategy map the specific objectives related to each perspective/dimension combination and then associate a performance indicator with each objective.

The resulting SV BSC is composed of indicators interconnected along different perspectives and dimensions. The application of the approach to the real case proves its feasibility and highlights characteristics, advantages and disadvantages of the SV BSC when used at IE level. The authors also provide guidelines for its application to other IEs.

The study contributes to the research on PMS by introducing and applying to a real case an integrated approach to assess SV in IEs, overcoming the shortcomings of PMS framed for single firms. It can be of interest for both researchers in the field of ecosystems value creation and practitioners managing or promoting such complex structures.

This study aims to explore an emerging collection of smart building technologies, known as smart workplace solutions (SWS), in the context of facilities management (FM).

This study is based on semi-structured interviews with facility managers in Finland, Norway and Sweden who have deployed SWSs in their organizations. SWS features, based on empirical data from a previous study, were also used to further analyse the interviews.

It analyses the benefits that SWSs bring from the facility management point of view. It is clear that the impetus for change and for deploying SWS in the context of FM is primarily driven by cost savings related to reductions in office space.

This research has been conducted with a focus on office buildings only. However, other building types can learn from the benefits that facility managers receive in the area of user-centred smart buildings.

SWSs are often seen as employee experience solutions that are only related to “soft” elements such as collaboration, innovation and learning. Understanding the FM business case can help make a more practical case for their deployment.

SWSs are an emerging area, and this study has collected data from facility managers who use them daily.

This paper aims to identify the archetypes of business models and illustrate how firms create, deliver and capture value by juxtaposing the firm’s aspired value emphasis with its strategic agility.

The two-by-two matrix is constructed based on an analysis of existing literature and conceptual development.

We advance a conceptualization of strategic agility to emphasize speed and flexibility as the main drivers, along with attention toward stakeholder expectations. Additionally, we unveil four different archetypes of business models based on the firm’s aspired value emphasis (economic vs plus social/environmental) and the type of strategic agility (defensive vs proactive).

Studies that empirically corroborate the proposed conceptualization of strategic agility are needed. In addition, empirical investigations on the evolutionary paths underlying the development of firms’ business models are requested.

Managers learn about aspects and actions that they should pursue to shift from one business model archetype to another.

We identify the features – in terms of focus on all the components of the triple bottom line (or not) and in terms of strategic agility – that firms need to face or even anticipate environmental and social transformation.

Identity regulation is part of a management control package. Organizations regulate employees’ self-identity to influence their behaviors. The success of this regulation depends on its trade-off with employees’ work identities and personalities. Organizational discourse nurtures this dynamic and interactive process. We focus on the regulation of an (undesired) organizational identity that is born at the intersection of race/ethnicity, gender, sex and migrant discrimination in accounting-related positions. We aim to analyze how Latina accountants who migrate to Spain perceive that their triple status as Latina, women and migrants affects their careers as accountants and interpret whether this triple intersectional discrimination aims to create a Latina accountant’s self-identity.

This critical study follows a phenomenological approach to analyze the experiences of women born in Latin America who migrated to Spain to occupy accounting-related positions. A thematic analysis of their semi-structured interviews allowed us to examine the challenges faced by Latina accountants in their accounting careers in Spain.

Our interviewees' narratives display an internalization of, even resignation to, a self-identity that we label “Latina accountant identity.” This identity is based on explicit discrimination discourses that cause them to suffer from the intersection of racism, sexism and migrant conditions and is nurtured by the discourses of their senior managers, co-workers and subordinates.

To the best of our knowledge, this is the first study to frame the regulation of an intersectional discriminatory identity that is used to control Latina accountants from the inside, acting on the triple condition of Latinas, women and foreigners, influencing their self-perceptions regarding work and personal lives.

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.