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Work-life flexibility policies (e.g., flextime, telework, part-time, right-to-disconnect, and leaves) are increasingly important to employers as productivity and well-being strategies. However, policies have not lived up to their potential. In this chapter, the authors argue for increased research attention to implementation and work-life intersectionality considerations influencing effectiveness. Drawing on a typology that conceptualizes flexibility policies as offering employees control across five dimensions of the work role boundary (temporal, spatial, size, permeability, and continuity), the authors develop a model identifying the multilevel moderators and mechanisms of boundary control shaping relationships between using flexibility and work and home performance. Next, the authors review this model with an intersectional lens. The authors direct scholars’ attention to growing workforce diversity and increased variation in flexibility policy experiences, particularly for individuals with higher work-life intersectionality, which is defined as having multiple intersecting identities (e.g., gender, caregiving, and race), that are stigmatized, and link to having less access to and/or benefits from societal resources to support managing the work-life interface in a social context. Such an intersectional focus would address the important need to shift work-life and flexibility research from variable to person-centered approaches. The authors identify six research considerations on work-life intersectionality in order to illuminate how traditionally assumed work-life relationships need to be revisited to address growing variation in: access, needs, and preferences for work-life flexibility; work and nonwork experiences; and benefits from using flexibility policies. The authors hope that this chapter will spur a conversation on how the work-life interface and flexibility policy processes and outcomes may increasingly differ for individuals with higher work-life intersectionality compared to those with lower work-life intersectionality in the context of organizational and social systems that may perpetuate growing work-life and job inequality.

This paper aims to compare the nature of three different business and management education (BME) research streams (online/blended learning, entrepreneurship education and experiential learning), along with their citation sources to draw insights on their support and legitimacy bases, with lessons on improving such support and legitimacy for the streams and the wider BME research field.

The authors analyze the nature of three BME research streams and their citation sources through tests of differences across streams.

The three streams differ in research foci and approaches such as the use of managerial samples in experiential learning, quantitative studies in online/blended education and literature reviews in entrepreneurship education. They also differ in sources of legitimacy recognition and avenues for mobilization of support. The underlying literature development pattern of the experiential learning stream indicates a need for BME scholars to identify and build on each other’s work.

Identification of different research bases and key supporting literature in the different streams shows important core articles that are useful to build research in each stream.

Readers will understand the different research bases supporting the three research streams, along with their targeted audience and practice implications.

The discovery of different support bases for the three different streams helps identify the network of authors and relationships that have been built in each stream.

According to the authors’ knowledge, this paper is the first to uncover differences in nature and citation sources of the three continuously growing BME research streams with recommendations on ways to improve the support of the three streams.

This study aims to propose and evaluate a novel framework of strategic adaptability in dyadic negotiations. The authors define strategic adaptability as a reaction to a cue that leads to shifts between integrative and distributive strategies. Based on the literature on turning points, phase models and strategic negotiations, the authors developed an initial framework identifying five distinct strategic adaptations.

To verify the framework, the authors analyzed two negotiation simulations with a diverse set of negotiation students. Negotiations were content-coded, and adaptations were labeled.

The authors found a consistent pattern across two studies. Overall, 12% (study 1) and 18% (study 2) of all speaking turns were identified as strategic adaptations. The findings empirically confirmed four of their strategic adaptation types: adapt to deadlock, follow adaptation by opponent, adapt to priority of issue under discussion and adapt to new information on issue. Moreover, findings of this study revealed two new types of strategic adaptability: delayed adaptation to opponent and adapt to understand opponent. Study 2 additionally revealed that strategies vary with the negotiation phase, and negotiation outcome seems to benefit more from the constellation rather than the frequency of adaptations. Furthermore, lower-scoring negotiators tended to adapt to the opponent’s strategy instead of initiating a change in strategy.

The findings of this study provide preliminary insights into how strategic adaptations unfold. These findings present future research opportunities to further test the framework's robustness, increase the knowledge of individual and cultural factors, explore the relationship with negotiation outcomes and develop educational interventions to enhance strategic adaptability.

This study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It contributes to the extant literature with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the perceptions of digital archival expertise.

In this study a two-phase data collection consisting of four online focus groups was held to gather the opinions of international archives and digital preservation professionals (n = 16), that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template analysis, a style of thematic analysis.

Four main themes were identified: fitting AI into day to day practice; the responsible use of (AI) technology; managing expectations (about AI adoption) and bias associated with the use of AI. The analysis suggests that AI adoption combined with hindsight about digitisation as a disruptive technology might provide archival practitioners with a framework for re-defining, advocating and outlining digital archival expertise.

The volunteer basis of this study meant that the sample was not representative or generalisable.

Although the results of this research are not generalisable, they shed light on the challenges prospected by the implementation of AI in the archives and for the digital curation professionals dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved for future research.

Social return on investment (SROI) has received increasing attention, both academically and professionally, since it was initially developed by the Roberts Enterprise Development Fund in the USA in the mid-1990s. Based on a systematic review of the literature that highlights the potential and limitations related to the academic and professional development of the SROI model, the purpose of this study is to systematize the academic debate and contribute to the future research agenda of blended value accounting.

Relying on the preferred reporting items for systematic reviews and meta-analyses approach, this study endeavors to provide reliable academic insights into the factors driving the usage of the SROI model and its further development.

A systematic literature review produced a final data set of 284 studies. The results reveal that despite the procedural accuracy characterizing the description of the model, bias-driven methodological implications, availability of resources and sector specificities can influence the type of approach taken by scholars and practitioners.

To dispel the conceptual and practical haze, this study discusses the results found, especially regarding the potential solutions offered to overcome the SROI limitations presented, as well as offers suggestions for future research.

This study aims to fill a gap in the literature and enhance a conceptual debate on the future of accounting when it concerns a blended value proposition.

This study examines the role of behavioural factors, such as confidence, optimism, pessimism and rational expectation, in affecting investment decisions in the Pakistani stock market.

Using daily trading data of Karachi Stock Exchange-100 index from January 2012 to December 2015, different regression models, including descriptive statistics and stationarity tests, are performed.

Results indicate that stock market trading has suffered from pessimistic behaviour of investors. In the first model, the authors find a positive sign of confidence and negative sign of optimism with the trading volume. The second model shows a positive role of confidence and rational expectations in affecting the trading volume in daily, Monday and Friday samples. The results of the third model show a negative sign of both optimism and rational expectation with the trading volume. Furthermore, the next model shows a negative sign of confidence combined with pessimism while testing their relationship with the trading volume. Finally, results of the final model suggest that optimism negatively affects the trading volume, and on the other hand, pessimism has a positive impact on the trading volume.

The method and empirical testing of behavioural biases and their relationship with economic variable used in this study seem to be a promising way to better understand the role of psychology in deriving financial decisions for academics and policymakers.

This study uses secondary data for measuring behavioural biases and decomposes the effect between rational expectation and behavioural biases.