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This paper investigates gender disparity in investment decisions within the popular American TV show Shark Tank.

The research uses a comprehensive dataset of 925 pitches from 14 seasons and 316 episodes, covering August 2009 to May 2023.

Contrary to previous studies, the findings indicate that female entrepreneurs do n'ot face discrimination in terms of their pitching success rates, regardless of their industry affiliation. However, the authors did observe that female entrepreneurs tend to receive lower valuations, both self-assessed and in final deals. This suggests a self-imposed gender gap in venture capital and angel investing, likely stemming from lower entrepreneurial aspirations among women.

To tackle this issue, the authors propose promoting female venture capital by increasing the representation of female entrepreneurs and business angels on Shark Tank. Such role models can inspire aspiring women in these fields. Additionally, the authors believe that mixed-gender founder teams, comprising both men and women, can play a significant role in developing promising startups with viable business models.

This study combines different perspectives on herding, viewing it as a social network heuristic in comparison to other heuristics. The purpose is to use the heuristic view of herding as found in early literature and test it on grounds of efficiency and payoff, in essence, combining the heuristic and rational agent view of herding. The simulated double auction setting includes agents embedded in a social network, allowing for an examination of herding alongside rational behaviour and imperfect signals.

In each round of the simulation, levels of homophily, density and fractions of types of agents is set and agents are allowed to follow their respective heuristics under those conditions. Characteristics of the social network, such as the size, levels of different homophilies, density and fractions of different types of agents are varied randomly to gauge their effect on the performance of herders vis-à-vis others and the overall market efficiency through simulation based approach. The data used for the study has been developed in Python and linear models are estimated using R.

Herding decreases total surplus in private value double auctions, but herders are not worse off than other agents and perform equally in common value auctions. Further, herders and random offerers reduce payoffs of other agents as well, and herding effects the surplus per transaction and not the quantum.

This study explores herding as a strategic behaviour coexisting with rationality and other strategies in specific circumstances. It presents intriguing findings on the impact of herding on individual outcomes and market efficiency, raising new avenues for future research. Implication to research includes a dent on the “sieve” argument of markets rooting out irrationality and from it, a policy implication that follows is the need for corrective measures as markets cannot self-correct this, given herders do not perform worse than others.

The study links the phenomenon of herding to the dynamics of social networks and heuristic-based learning mechanisms that sets apart this research from the majority of existing literature, which predominantly conceptualizes herding as an outcome derived from a perfect Bayesian Equilibrium and a rational learning process.

Despite the growing emphasis on sustainability and the need to manage environmental, social, and governance (ESG) risks, the direct relationship between enterprise risk management (ERM) and green growth (GG) has not been investigated. This study seeks to fill this gap by examining the effect of ERM on the GG of oil and gas (O&G) companies in Malaysia.

The study used panel data regression models to analyze panel data from 2012 to 2021. For computing GG, we adapted the Organization for Economic Cooperation and Development’s (OECD) GG framework. ERM is computed using COSO and WBCSD guidelines for ESG-related risks. Weighted content analysis is used to measure ERM and GG

The findings derived from the content and descriptive statistics analyses indicate a consistent and ongoing rise in the adoption of ERM practices over time. However, some companies are still in the initial stages of incorporating ERM to address ESG risks. The study’s findings unequivocally establish a substantial and positive relationship between ERM and GG. ERM drives GG by significantly influencing its environmental and resource productivity dimensions. The study further reveals that the impact of ERM on economic opportunities and policy responses, as well as the natural asset base, is statistically significant, albeit with relatively lower coefficient values.

To enhance the legitimacy of organizations and foster positive stakeholder relationships, regulators, governments, and policymakers should actively promote the adoption of ERM standards that specifically address ESG risks, as outlined by COSO and WBCSD. This strategic alignment with risk management practices will ultimately contribute to improving green growth for organizations.

To the best of the authors' knowledge, this is the first study examining ERM’s effect on GG. The study adds to the existing literature by focusing on ERM’s role in a company’s GG. It clarifies ERM’s significant effect on diminishing emerging ESG risks and advancing GG

Home-based commercial hospitality (HBCH) is the focus of this study. This community-based tourism (CBT), which has received little research attention, is examined to reveal the meaning of commercially hosting visitors in private homes for experiential meetings on a variety of topics such as food, art, culture, folklore and various workshops.

A qualitative research method was adopted, using semi-structured, in-depth interviews with HBCH providers in the desert town of Arad, located in southern Israel.

The study reveals the impact of this unusual occupation on the host's quality of life, the factors that encourage and suppress involvement in this entrepreneurship, as well as the positive and negative consequences of HBCH on the local environment.

The findings could offer important guidelines to municipalities and local governments seeking to encourage CBT and sustainable micro-enterprises.

HBCH is a recent phenomenon and, as such, has been little researched. This study of one community raises issues that may be shared by HBCH enterprises. The findings could contribute to developing such initiatives elsewhere, avoiding the obstacles faced in this pioneering effort.

Exploring trust's impact on AI project success. Companies can't leverage AI without employee trust. While analytics features like speed and precision can build trust, they may also lower it during implementation, leading to paradoxes. This study identifies these paradoxes and proposes strategies to manage them.

This paper applies a grounded theory approach based on 35 interviews with senior managers, users, and implementers of analytics solutions of large European companies.

It identifies seven paradoxes, namely, knowledge substitution, task substitution, domain expert, time, error, reference, and experience paradoxes and provides some real-life examples of managing them.

The limitations of this paper include its focus on machine learning projects from the last two years, potentially overlooking longer-term trends. The study's micro-level perspective on implementation projects may limit broader insights, and the research primarily examines European contexts, potentially missing out on global perspectives. Additionally, the qualitative methodology used may limit the generalizability of findings. Finally, while the paper identifies trust paradoxes, it does not offer an exhaustive exploration of their dynamics or quantitative measurements of their strength.

Several tactics to tackle trust paradoxes in AI projects have been identified, including a change roadmap, data “load tests”, early expert involvement, model descriptions, piloting, plans for machine-human cooperation, learning time, and a backup system. Applying these can boost trust in AI, giving organizations an analytical edge.

The AI-driven digital transformation is inevitable; the only question is whether we will lead, participate, or fall behind. This paper explores how organizations can adapt to technological changes and how employees can leverage AI to enhance efficiency with minimal disruption.

This paper offers a theoretical overview of trust in analytics and analyses over 30 interviews from real-life analytics projects, contributing to a field typically dominated by statistical or anecdotal evidence. It provides practical insights with scientific rigour derived from the interviews and the author's nearly decade-long consulting career.