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This research investigates how women entrepreneurs navigate socially embedded gender assumptions to achieve success in both for-profit and nonprofit sectors, focusing on their identity crafting and self-presentation strategies and exploring differences in needs fulfilment. The research employs self-determination theory (SDT) and interpretative phenomenology to qualitatively investigate interviews with female entrepreneurs to track their experiences, examining identity construction, gender challenges, and breakthrough innovation. A systematic coding process reveals themes of gendered assumptions, identity, and innovation, juxtaposed with the pillars of autonomy, competence, and relatedness, ultimately aiming to understand the unique barriers and innovative strategies for women entrepreneurs. The interviews reveal their struggles against gendered assumptions, efforts to assert identity, use of innovation, and alignment with SDT’s psychological needs. They emphasise challenges, the importance of authenticity, and innovation’s role in challenging biases while highlighting how autonomy, competence, and relatedness needs are fulfilled. Overall, the interviews offer insights into women’s entrepreneurial experiences and strategies for success. This research explores female entrepreneurship as a distinct domain, shedding light on the nuanced variations in autonomy, competence, and relatedness needs fulfilment. By emphasising the necessity for customised support structures and inclusive environments, the study offers a fresh perspective on empowering women entrepreneurs, adding depth to our understanding of gender dynamics in entrepreneurship and underscoring the importance of tailored interventions.

With the increasing role of computing devices, facilitating natural human computer interaction (HCI) will have a positive impact on their usage and acceptance as a whole. For long time, research on HCI has been restricted to techniques based on the use of keyboard, mouse, etc. Recently, this paradigm has changed. Techniques such as vision, sound, speech recognition allow for much richer form of interaction between the user and machine. The emphasis is to provide a natural form of interface for interaction. Gestures are one of the natural forms of interaction between humans. As gesture commands are found to be natural for humans, the development of gesture control systems for controlling devices have become a popular research topic in recent years. Researchers have proposed different gesture recognition systems which act as an interface for controlling the applications. One of the drawbacks of present gesture recognition systems is application dependence which makes it difficult to transfer one gesture control interface into different applications. This paper focuses on designing a vision-based hand gesture recognition system which is adaptive to different applications thus making the gesture recognition systems to be application adaptive. The designed system comprises different processing steps like detection, segmentation, tracking, recognition, etc. For making the system as application-adaptive, different quantitative and qualitative parameters have been taken into consideration. The quantitative parameters include gesture recognition rate, features extracted and root mean square error of the system while the qualitative parameters include intuitiveness, accuracy, stress/comfort, computational efficiency, user's tolerance, and real-time performance related to the proposed system. These parameters have a vital impact on the performance of the proposed application adaptive hand gesture recognition system.

The aim of this paper is to examine the potential differences in the entrepreneurial self-efficacy (ESE) of female entrepreneurs who own businesses in gender congruent and incongruent industries. Through the lens of social role theory the four antecedents of efficacy are examined. The results of ten in-depth, qualitative interviews show there are overarching similarities in all factors between the groups but nuanced differences in the areas of mastery experiences, social persuasion and vicarious learning. Differences were also found in the areas of social comparisons and perceived gender stereotypes. The paper discusses how ESE manifests for female entrepreneurs based on the gender congruency of the industry in which her business operates. Last, areas for future research are discussed.

Healthcare entrepreneurs (HCEs) develop and implement solutions to improve processes, structures, and practices. Unfortunately, they also operate in environments with multiple levels of uncertainty because of their industry’s unpredictable, nonlinear, and risk-averse features. Moreover, entrepreneurial uncertainty has been underexplored in the healthcare context. This empirical study examines HCEs’ uncertainty management processes throughout the entrepreneurial process. In-depth interviews were conducted with 25 for-profit and social HCEs across nine sectors in nineteen different states within the United States. We used a mixed methods qualitative approach to analyze, interpret and visualize data, including constant comparative and lexical analyses. Interview data reveals HCEs experience five major uncertainties and utilize distinct strategies to manage those uncertainties. Findings highlight the effect of external environmental conditions on HCEs’ uncertainty, the complexity and interconnectedness of HCEs’ uncertainties, their willingness to utilize a wide repertoire of strategies to cope with uncertainty, and communication’s central role in the management process. We provide practical advice to improve educational, training and professional development initiatives for aspiring, novice and experienced HCEs.

The issue of boundary determination in ecosystems remains problematic which is exaggerated in a dynamic, emerging innovation context, with actors joining and leaving. As part of wider research, on how firms innovate in emerging ecosystems, the boundaries of several early innovation ecosystems were explored.

Using evolutionary approaches, with extensive interviews and mapping, the wider ecosystem was initially researched. Then, five in-depth firm-focussed case studies were undertaken, and specific innovation ecosystem boundaries were mapped as they emerged and evolved.

The findings point to ‘identity’, a common early approach, being limited as the ecosystem evolves. The influence of competence and relationships play an increasing role. It is suggested that as the innovation ecosystem develops through its lifecycle, different approaches aligned to Santos and Eisenhardt’s (2005) four foci can be employed, starting with identity, then competence, then power and finally issues of efficiency.

We propose that businesses, government, and not-for-profit entities could benefit from a better understanding of organizational behavior through the lens of a contemporary global culture model. Human resourcing and partnering decisions could be improved by using global culture to ensure a better organizational-fit as well as to reduce the risk of destructive relationship dependencies. For an extreme-limits example, a company could inadvertently hire a terrorist or a social loafer seeking to steal competitive intelligence. A big data approach supported by a socio-cultural framework could help in hypothesis testing which is essential for advancing the body of knowledge in organizational behavior. This paper will make a scholarly contribution by identifying literature relevant to collecting and analyzing organizational big data that could explain beneficial socio-cultural behavior. This paper will explore how sources of qualitative big data could be collected and then analyzed to measure organizational-fit factors relevant for decision-making.

In an article added recently to the Stanford Encyclopedia of Philosophy, Eran Tal notes that “there is little consensus among philosophers as to how to define measurement, what sorts of things are measurable, or which conditions make measurement possible” [1]

The meaning and scope of measurement have been discussed frequently within physical metrology, by David Hand [2], René Dybkaer [3], Rod White [4], Eran Tal [5], Luca Mari et al. [6], Giovanni Rossi [7], Eran Tal [8], and Luca Mari et al. [9], among others. The same issues have also been examined within the social sciences [10], in particular in education [11, 12], psychology [13–15], and sociology [16, 17].

Defining measurement involves identifying its essential characteristics, and specifying the traits that distinguish it from similar activities that ought not to be construed as measurement. Delineating its scope involves determining the classes of properties whose values are measurable.

None of the logical, terminological, historical, or customary-use considerations that might give preeminence to a particular definition of measurement seem to be decisive, for otherwise a consensus about it would have formed already.

For example, a recent survey of its membership, conducted by the ISO/REMCO committee on reference materials, revealed that the metrological community represented in this committee is just about evenly split on whether the assignment of value to qualitative (nominal, or categorical) properties should, or should not be called "measurement."

Defining the meaning of measurement, and circumscribing its scope, therefore involve arbitrary choices, which concern matters of taste, matters of precedence, and matters of convenience. This contribution discusses these choices, and advocates for an inclusive and broad understanding of measurement.

The metrology of what might be called 'qualitative' measurements is an area of increasing interest in many fields but is a challenging area because the usual tools for handling e.g. measurement uncertainty based on simple probability variance calculations do not always work. Basically, measurement uncertainty arises wherever information is lost somewhere in the measurement system. Therefore, in this chapter we offer an information theoretical approach to the fundamental treatment of measurement uncertainty in qualitative measurements, such as on an ordinal scale. Starting with the basic syntax of measurement information, we progress towards successively richer information formulations, including semantic and pragmatic approaches. Ultimately it is how the measurement results are to be used – that is, the 'effectiveness' of measurement information in decision-making in conformity assessment – which determines the most appropriate way of handling uncertainties, the associated risks of incorrect decisions of compliance and the impact consequences of such decisions. Topical examples include the study of human perception of accessibility and product function, for instance for the disabled and in healthcare.