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The lean startup approach (LSA) is extensively utilized by early-stage entrepreneurs, with “pivot” serving as a key pillar. However, there is a research gap concerning the boundary conditions impacting LSA and pivot decisions, especially when addressing societal challenges, as in the context of transformational entrepreneurship. In this regard, the healthcare sector, further compounded by a lack of research on startups and scale-ups, presents an embraced opportunity to provide multiple contributions for both theory and practice.

The present investigation employs a grounded approach to explore the experiences of the co-founders of a fast-growing Italian e-health startup. A narrative strategy was employed to organize conditions and evolving strategic action/interactions into three different pivoting phases of the startup – before the pivot, its enactment and aftermath – with primary and secondary data collected over a period of one year.

Pivoting in digital healthcare unfolded as a liminal experience marked by factors such as high regulation, multiple stakeholders, technological and symbolic ambivalence, resource-intensive demands and institutional actors acting as pathway pioneers, leading to an information overload and unforeseeable uncertainty to manage. These factors challenge entrepreneurs' ability to attain optimal distinctiveness, presenting the paradoxical need for vertical flexibility for scaling up.

By uniquely illuminating the sector’s constraints on entrepreneurial phenomena, this study provides a valuable guide for entrepreneurs and institutional actors in addressing societal challenges.

This study introduces a process model of transformational information crafting when pivoting, highlighting the role of entrepreneurs' transformational stance and platform-mediated solutions as engines behind strategies involving information breaking and transition, preceding knowledge-driven integration strategies.

By investigating the reactions of family businesses to COVID-19 pandemic this article aims to explaining how family firms are capable to preserve employment during hardship.

Stemming from resource-based-view, we theorise that familiness is not directly associated with new hiring but instead fully mediated by pivoting strategic decisions (the propensity to transform the business).

Our findings show that familiness triggers pivoting strategic decisions and consequently increases the likelihood of new hiring. Additionally, we found that the involvement of multiple generations strengthens this relationship.

Family firms must consolidate their family human and social resources (familiness) and assure the presence of multiple generations in the firm because they can leverage their entrepreneurial disposition and increase the need to preserve employment and new hires during crises.

The main contribution lies in the explanation of the mechanisms that family firms deploy to overcome a crisis and thus explains why some family firms are more resilient than others in relation to firm’s employment during hardship.

Sorting is a very important algorithm to solve problems in computer science. The most well-known divide and conquer sorting algorithm is quicksort. It starts with dividing the data into subarrays and finally sorting them.

In this paper, the algorithm named Dual Parallel Partition Sorting (DPPSort) is analyzed and optimized. It consists of a partitioning algorithm named Dual Parallel Partition (DPPartition). The DPPartition is analyzed and optimized in this paper and sorted with standard sorting functions named qsort and STLSort which are quicksort, and introsort algorithms, respectively. This algorithm is run on any shared memory/multicore systems. OpenMP library which supports multiprocessing programming is developed to be compatible with C/C++ standard library function. The authors’ algorithm recursively divides an unsorted array into two halves equally in parallel with Lomuto's partitioning and merge without compare-and-swap instructions. Then, qsort/STLSort is executed in parallel while the subarray is smaller than the sorting cutoff.

In the authors’ experiments, the 4-core Intel i7-6770 with Ubuntu Linux system is implemented. DPPSort is faster than qsort and STLSort up to 6.82× and 5.88× on Uint64 random distributions, respectively.

The authors can improve the performance of the parallel sorting algorithm by reducing the compare-and-swap instructions in the algorithm. This concept can be used to develop related problems to increase speedup of algorithms.

This study sheds light on the impact of coronavirus disease 2019 (COVID-19) on self-employed women in Mauritius and delves into their coping strategies amid unprecedented circumstances.

The study employs a comprehensive methodology encompassing both qualitative and quantitative approaches. Qualitative analysis is conducted via interviews with self-employed women in Mauritius, capturing their experience with COVID-19. Quantitative analysis uses survey data from 732 firms to investigate factors affecting online sales, a major survival strategy adopted by businesses.

The study finds a notable trend wherein many interviewed women have transitioned their businesses online – a pivot that stands out as a common response within the sample. The quantitative analysis indicates the significance of a strong online presence, particularly on social media and web-based platforms designed for the sale of goods and services. These factors correlate with higher online sales, potentially enhancing resilience during pandemic-induced lockdown periods.

This study stands out for its originality, representing the first study investigating how women entrepreneurs in Mauritius have navigated the challenges posed by the COVID-19 crisis.

Patients having ventricular arrhythmias and atrial fibrillation, that are early markers of stroke and sudden cardiac death, as well as benign subjects are all studied using the electrocardiogram (ECG). In order to identify cardiac anomalies, ECG signals analyse the heart's electrical activity and show output in the form of waveforms. Patients with these disorders must be identified as soon as possible. ECG signals can be difficult, time-consuming and subject to inter-observer variability when inspected manually.

There are various forms of arrhythmias that are difficult to distinguish in complicated non-linear ECG data. It may be beneficial to use computer-aided decision support systems (CAD). It is possible to classify arrhythmias in a rapid, accurate, repeatable and objective manner using the CAD, which use machine learning algorithms to identify the tiny changes in cardiac rhythms. Cardiac infractions can be classified and detected using this method. The authors want to categorize the arrhythmia with better accurate findings in even less computational time as the primary objective. Using signal and axis characteristics and their association n-grams as features, this paper makes a significant addition to the field. Using a benchmark dataset as input to multi-label multi-fold cross-validation, an experimental investigation was conducted.

This dataset was used as input for cross-validation on contemporary models and the resulting cross-validation metrics have been weighed against the performance metrics of other contemporary models. There have been few false alarms with the suggested model's high sensitivity and specificity.

The results of cross validation are significant. In terms of specificity, sensitivity, and decision accuracy, the proposed model outperforms other contemporary models.

This study aims to answer the question: What dynamic capabilities do diverse humanitarian organizations have?

We examine this question through the lens of dynamic capabilities with sensing, seizing and reconfiguring capacities. The research team interviewed 15 individuals from 12 humanitarian organizations that had (a) different geographic scopes (global versus local) and (b) different missions (emergency response versus long-term development aid). We also gathered data from secondary sources, including standard operating procedures, company websites, and news databases (Factiva, Reuters and Bloomberg).

The findings identify the operational and dynamic capabilities of global and local humanitarian organizations while distinguishing between their mission to provide long-term development aid or emergency relief. (1) The global organizations, with their beneficiary responsiveness, reconfigured their sensing and seizing capacities throughout the COVID-19 pandemic by pivoting quickly to local procurement or regional supply chains. The long-term development organizations pivoted to multi-year supplier agreements with fixed pricing to counter price uncertainty and accessed social capital with government bodies. In contrast, emergency response organizations developed end-to-end supply chain visibility to sense changes in supply and demand. (2) Local humanitarian organizations developed the capacity to sense demand and supply changes to reconfigure based on their experiential learning working with the local community. The long-term-development local organizations used un-owned and scalable relief infrastructure to seize opportunities to rebuild affected areas. In contrast, emergency response organizations developed their capacity to seize opportunities to provide aid stemming from their decentralized decision-making, a lack of structured procedures, and the authority for increased expenditure.

We propose a theoretical framework to identify humanitarian organizations' operational and dynamic capabilities, distinguishing between global and local organizations and their emergency response and long-term aid missions.

Universities globally have faced the introduction of research performance assessment systems that provide monetary and ranking rewards based on publication outputs. This study aims to seek an understanding of the implementation of performance-based research funding (PBRF) and its impact on the heads of departments (HoDs) and accounting academics in New Zealand (NZ) tertiary institutions. The study explores NZ accounting academics’ experiences and their workload; the relationship between teaching and research in the accounting discipline and any issues and concerns affecting new and emerging accounting researchers because of PBRF.

Applying an institutional theoretical lens, this paper explores accounting HoDs’ perceptions concerning the PBRF system’s impact on their academic staff. The research used semi-structured interviews to collect data from NZ’s eight universities.

The key findings posit that many institutional processes, some more coercive in nature, whereas others were normative and mimetic, have been put in place to ensure that academics are able to meet the PBRF requirements. HoDs suggest that their staff understand the importance of research, but that PBRF is a challenge to new and emerging researchers and pose threats to their recruitment. New academics must “hit the ground running” as they must demonstrate not only teaching abilities but also already have a track record of research publications; all in all, a daunting experience for new academics to overcome. There is also a teaching and research disconnect. Furthermore, many areas where improvements can be made in the design of this measurement tool remain.

The PBRF system has significantly impacted on accounting academics. Central university research systems were established that subsequently applied coercive institutional pressures onto line managers to ensure that their staff performed. This finding offers scope for future research to explore a better PBRF that measures and rewards research productivity but without the current system’s unintended negative consequences.

The purpose of this study was to examine the preservice educational narratives of Black English teachers in an effort to determine their experiences within teacher education programs with assigned white cooperating teachers.

Drawing upon Black storytelling, testimony and breath in narrative analysis, this study showcases how Black preservice teachers navigated regularized surveillance and abandonment as part of student teaching practicum.

The authors argue that, in response to their treatment, these Black preservice teachers created resistance strategies as a way to fill the mentorship void and sustain their own future teaching careers.

The literature on Black preservice teachers does the critical work of examining how they experience their racial, linguistic and gendered identities in the classroom; however, this study focuses on their experiences with white cooperating teachers – an underresearched area in the past 10 years.

Pregnant employees experience considerable interpersonal discrimination. This study explores the range of possible reactions of observers to pregnancy self-disclosure, interpersonal discrimination and various allyship interventions, and the attentional processes that lead to those reactions. Consequently, it uncovers socio-cognitive processes underlying support for and backlash toward pregnancy in the workplace.

This study used a thought-listing technique to explore observers’ spontaneous thoughts related to pregnancy. Working adults were randomly assigned to read through one of the six scenarios depicting pregnancy self-disclosure, interpersonal discrimination and male allyship interventions (i.e. stating the organization’s anti-discrimination policy, confronting the transgressor by calling out sexism, pivoting the conversation to highlight the strengths of the pregnant employee and a hybrid intervention combining highlighting strengths and confrontation) after which participants listed the top three thoughts that came to their mind (1,668 responses). Responses were thematically analyzed to explore spontaneous reactions toward the pregnant employee, transgressor and ally in the scenario.

Surprisingly, across all scenarios, the most sexist thoughts emerged during pregnancy self-disclosure, even in the absence of any transgression. After a transgression occurred, any allyship intervention was better than none in eliciting lesser sexist backlash against the pregnant employee. Stating the organization’s anti-discrimination policy was most beneficial for the pregnant employee in eliciting the least sexist backlash but at the cost of generating unfavorable impressions of the ally. Calling out the transgressor’s bias elicited the most sexist backlash toward the pregnant employee, yet it created favorable impressions of the ally. In contrast, highlighting the strengths of the pregnant employee created the most favorable impression of the ally while eliciting a few sexist thoughts about the pregnant employee. Overall, the hybrid intervention was the most effective at balancing the competing goals of generating support for the pregnant employee, creating favorable impressions of the ally, as well as holding the transgressor accountable.

This study demonstrates that the type of allyship intervention critically redirects the attentional focus of observers to certain aspects of a discrimination episode and relevant schemas which can generate support or backlash toward targets, transgressors and allies, thereby advancing or obstructing equity and inclusion in organizations.

Advances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.

This study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.

The results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.

The formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.

This study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.