This study examines social media's significant yet challenging role as a tool for entrepreneurial success among Black women entrepreneurs. Entrepreneurship is a powerful pathway to social and economic mobility, offering underrepresented groups opportunities for financial independence and influence. Social media platforms have opened up avenues for brand building, customer engagement, and business scaling. Still, Black women face obstacles in leveraging these opportunities such as algorithmic bias, underrepresentation, and social barriers. This paper explores these challenges and the strategic approaches Black women entrepreneurs can employ to build a robust social media presence despite systemic limitations. It also provides policy recommendations to mitigate these barriers' impact and calls for targeted support from government and private sector initiatives, including education on platform algorithms and inclusive design. Finally, empirical research directions are proposed to deepen understanding of how Black women utilize social media for opportunity recognition and business performance.

Business accelerators play a key role in the initial critical stages of assessment of commercial viability, offering mentorship provision of funding and protection of intellectual property (IP) for product development and refinement. However, little is known about the decision-making criteria and detailed analysis of the underlying criteria and interdependencies between the key factors used by accelerator organizations to fund start-ups. This article focuses on the decision-making criteria utilized by a leading £21M accelerator program, largely funded by the European Regional Development Fund for initial stage funding and IP protection for product and innovation commercialization. We incorporate a multimethodological interpretive-based approach based on Day's "Real-Win-Worth" framework to develop the interrelationships and ranking between the factors. The results highlight the significance and weighting attached to the factors associated with the technical competency of the proposer and evidence of demand existing for the product. We propose a new framework that models the key factor interrelationships offering additional insight to accelerator-based decision making.

INTRODUCTION January 1, 2021, the Centers for Medicare and Medicaid Services (CMS) implemented a Hospital Price Transparency Rule. Consumerism as a means of reducing healthcare expenditures is predicated on informed consumers making discrete choices. METHODS For 10 months, immediately following an academic medical center preoperative clinic visit, patients and their surgeons were surveyed regarding their estimation of hospital cost and hospital reimbursement for the upcoming surgery. Responses were compared to average FY 2019 institutional Cost for Medicare patients undergoing a laparoscopic approach for each operation. We calculated the difference between actual reimbursements and costs with patients' estimates and actual reimbursements and costs with surgeons' estimates. RESULTS 66 questionnaires were collected from patients who underwent laparoscopic: cholecystectomy (n=20), inguinal hernia (n=17), umbilical hernia repair (n=6), ventral hernia repair (n=6), incisional hernia (n=6), hiatal hernia repair (n=1), and lipoma or cyst excision (n=10). Patients' estimates for hospital costs exceeded actual hospital costs by a median of $4502 and were less than hospital reimbursements by a median of $1834. Surgeon estimates for direct costs were $825 less than hospital direct costs and $1659 less than hospital reimbursement. CONCLUSION Patients as well as their surgeons do not estimate healthcare costs or remuneration accurately and therefore will be ineffective change agents in reducing surgical spending based on price transparency without further education of both parties. Patients consistently overestimated surgical costs while surgeons consistently underestimated surgical costs and reimbursements. Better-informed surgeons and patients are likely necessary prerequisites for CMS Price Transparency Rules to be effective in reducing Medicare expenditures in surgery.

On February 5, 2022, the field of augmentative and alternative communication (AAC) lost a giant when Dr. David "Dave" Beukelman passed away. As the readership of this journal is aware, Dave was one of the principal founders of the AAC field and devoted his career to providing a voice to those without one. Before AAC became a field, people who could not talk were invisible or seldom noticed, unless they were in the way. For more than 40 years, he was a catalyst for change in AAC clinical practice, research, dissemination, teaching, and public policy development. This tribute aims to honor Dave's lifelong mission of serving others by sharing some of his most timeless and valued lessons. Each lesson begins with one of Dave's most enduring quotes that is then followed by a brief synopsis of the lesson Dave hoped to convey.

Military and commercial users of energetic materials strive for reliability and safety improvements while balancing the requirement to reduce environmental impact. The industry also consistently faces regulatory pressure to reduce or eliminate a variety of hazardous substances from the work flow. Alternatives are now available to the industry that offer either product improvements in the form of increased safety or performance or represent more environmentally friendly alternatives for product development. Pacific Scientific Energetic Materials Co. has been actively involved with a variety of programs to develop new energetic materials with improved performance or reduced environmental impact to provide alternatives to widely used primary explosives. The current status in development of these materials is provided.

Purpose The UK Government-funded National Health Service (NHS) is experiencing significant pressures because of the complexity of challenges to, and demands of, health-care provision. This situation has driven government policy level support for transformational change initiatives, such as value-based health care (VBHC), through closer alignment and collaboration across the health-care system-life science sector nexus. The purpose of this paper is to evaluate the necessary antecedents to collaboration in VBHC through a critical exploration of the existing literature, with a view to establishing the foundations for further development of policy, practice and theory in this field. Design/methodology/approach A literature review was conducted via searches on Scopus and Google Scholar between 2009 and 2019 for peer-reviewed articles containing keywords and phrases “Value-based healthcare industry” and “healthcare industry collaboration”. Refinement of the results led to the identification of “guiding conditions” (GCs) for collaboration in VBHC. Findings Five literature-derived GCs were identified as necessary for the successful implementation of initiatives such as VBHC through system-sector collaboration. These are: a multi-disciplinarity; use of appropriate technological infrastructure; capturing meaningful metrics; understanding the total cycle-of-care; and financial flexibility. This paper outlines research opportunities to empirically test the relevance of the five GCs with regard to improving system-sector collaboration on VBHC. Originality/value This paper has developed a practical and constructive framework that has the potential to inform both policy and further theoretical development on collaboration in VBHC.

As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.

Collaboration between industry and academia necessitates the management of entrepreneurial dynamics within ecosystem contexts. However, such partnerships perpetuate numerous challenges that, without effective management, can impact upon the ecosystem as a whole. Limited research to date has addressed the challenges affecting these university-industry partnerships and ascertained their impact upon ecosystem management. This study identifies the challenges pervading university-industry partnerships across entrepreneurial ecosystems, with a view that through an exposition of such challenges, more specific strategies could be implemented to address them. Questionnaires were distributed to key ecosystem stakeholders, requesting their perceptions of the key challenges affecting their collaborative relationships. Empirical data was analysed utilising fuzzy-set qualitative comparative analysis to deduce the configurational nature of the conditions. Results reveal mutually exclusive solutions grounded upon distinct combinations of conditions, constituting distinct pathways to ineffective ecosystem management. Theoretical and practical implications are discussed, as well as acknowledged limitations of this study and suggestions for future research.

With an estimated 2.46 billion social media users globally, the commercial potential for social commerce is clear. Fundamental aspects of social commerce include making and receiving payments with users feeling secure when doing so, and ensuring the site is enjoyable and easy to use. As social commerce migrates to the mobile platform, perceptions of these elements within a mobile context have become of paramount importance. Correspondingly, this study extends existing knowledge by employing a context theory contextualization approach to develop two research models to investigate user perceptions of payments, security (in terms of risk and trust), and ease of use within a mobile context. Empirical data were analyzed using variance-based structural equation modeling, including multi-group analysis to explore possible differences based on gender, age, and method used to pay for mobile services. Results reveal that perceived innovativeness is a key success factor, followed by perceived usefulness, and convenience. Perception of a secure environment is only of partial influence. No differences were found based upon gender as a moderator, whereas age and method used to pay for mobile services both revealed differences in results. Theoretical and practical contributions are presented, along with acknowledged limitations and suggestions for further work.

Organisations are increasingly creating inter-organisational ecosystem partnerships to innovate openly. Despite effective knowledge management significantly supporting ecosystem infrastructures, empirical insights into the importance of and interdependencies between conditions for successful knowledge exchange across ecosystem contexts remain unexplored within existing literature. This study implements a mixed-method approach to ascertain which conditions are responsible for knowledge transfer success across innovation ecosystems. Interpretive Structural Modelling was employed to analyse questionnaires with key ecosystem stakeholders, in order to impose a hierarchical structure upon the conditions. The configurational nature of these conditions, and their combinations into solutions for success was ascertained through analysing semi-structured interviews using fuzzy-set Qualitative Comparative Analysis. Results reveal multiple, mutually exclusive pathways to knowledge transfer success, grouped into three solution types, increasing understanding of the interrelated nature of the knowledge transfer conditions. Limitations and implications for future research are provided.

The technological choices facing the manufacturing industry are vast and complex as the industry contemplates the increasing levels of digitization and automation in readiness for the modern competitive age. These changes broadly categorized as Industry 4.0, offer significant transformation challenges and opportunities, impacting a multitude of operational aspects of manufacturing organizations. As manufacturers seek to deliver increased levels of productivity and adaptation by innovating many aspects of their business and operational processes, significant challenges and barriers remain. The roadmap towards Industry 4.0 is complex and multifaceted, as manufacturers seek to transition towards new and emerging technologies, whilst retaining operational effectiveness and a sustainability focus. This study approaches many of these significant themes by presenting a critical evaluation of the core topics impacting the next generation of manufacturers, challenges and key barriers to implementation. These factors are further evaluated via the presentation of a new Industry 4.0 framework and alignment of I4.0 themes with the UN Sustainability Goals.

The effective management of knowledge exchange is critical for open innovation in ecosystem contexts where organizations may partner with potential competitors. This study contributes to existing knowledge by detecting the conditions for knowledge transfer success between both coopetitive and non-competitive ecosystem partners. The study uses a qualitative approach. Semi-structured interviews were conducted with 20 stakeholders across multi-industry ecosystems to compare the presence of knowledge transfer conditions between competitors and non-competitors. Through fuzzy-set qualitative comparative analysis (fsQCA), configurational recipes of conditions were identified, revealing the distinct configurations required of either coopetitive or non-competitive partnerships in the context of innovation ecosystems. The findings show the need for organizations to tailor knowledge exchange practices to the competitive nature of each relationship. Notable theoretical and practical implications are provided for ecosystem stakeholders that engage in coopetitive partnerships to develop innovations.

Much of the recent work in the field of smart manufacturing is dependent on a data-driven approach, with an increasing number of data-hungry techniques being introduced to improve the adaptability and productivity of manufacturing systems.

Background: Fostering medical students' appreciation for team members particularly those from other disciplines with varying levels of experience promotes a promising beginning to a health care career. Methods: During surgical clerkship orientation, third-year medical students completed 30-item TeamSTEPPS Teamwork Attitudes Questionnaire preintervention and postintervention, spent 7 min identifying errors in a simulated operating room, followed by recorded physician-led 30-min discussions. Results: Postintervention (67) compared with preintervention (141) mean TeamSTEPPS Teamwork Attitudes Questionnaire domain scores were statistically significantly higher for team structure (4.59, 4.70; P = 0.03) and higher but not significant for leadership (4.74, 4.75; P = 0.86), situation monitoring (4.62, 4.68; P = 0.32), communication (4.40, 4.50; P = 0.14), and decreased for mutual support (4.43, 4.36; P = 0.43). Medical students identified 2%-93% of 33 staged errors and 291 additional errors, which were placed into 14 categories. Soiled gloves in the operative field and urinary bag on the floor were the most frequently identified staged errors. Experienced nurses compared with medical students identified significantly more errors (mean, 17.7 versus 11.7, respectively; P < 0.001). Recognizing errors when lacking familiarity with the operative environment and appreciating teammates' perspectives were themes that emerged from discussions. Conclusions: This well-received teamwork exercise enabled medical students to appreciate team members' contributions and other disciplines' perspectives, in addition to the synergy that occurs with multidisciplinary teams. (C) 2020 Elsevier Inc. All rights reserved.

For many contemporary manufacturing processes, autonomous robotic operators have become ubiquitous. Despite this, the number of human operators within these processes remains high, and as a consequence, the number of interactions between humans and robots has increased in this context. This is a problem, as human beings introduce a source of disturbance and unpredictability into these processes in the form of performance variation. Despite the natural human aptitude for flexibility, their presence remains a source of disturbance within the system and make modelling and optimization of these systems considerably more challenging, and in many cases impossible. Improving the ability of robotic operators to adapt their behaviour to variations in human task performance is, therefore, a significant challenge to be overcome to enable many ideas in the larger intelligent manufacturing paradigm to be realised. This work presents the development of a methodology to effectively model these systems and a reinforcement learning agent capable of autonomous decision-making. This decision-making provides the robotic operators with greater adaptability, by enabling its behaviour to change based on observed information, both of its environment and human colleagues. The work extends theoretical knowledge on how learning methods can be implemented for robotic control, and how the capabilities that they enable may be leveraged to improve the interaction between robots and their human counterparts. The work further presents a novel methodology for the implementation of a reinforcement learning-based intelligent agent which enables a change in behavioural policy in robotic operators in response to performance variation in their human colleagues. The development and evaluation are supported by a generalized simulation model, which is parameterized to enable appropriate variation in human performance. The evaluation demonstrates that the reinforcement agent can effectively learn to make adjustments to its behaviour based on the knowledge extracted from observed information, and balance the task demands to optimise these adjustments.

Despite increasing consumer engagement with social commerce, conditions influencing user intention to utilize m-payment services on social media platforms remain unclear. This study employs fsQCA to ascertain configurations of conditions for behavioral intention, identifying solutions to assist theory and practice regarding the adoption of this form of commerce.

Background. The literature on unprofessional behavior is reviewed. It is well accepted that unprofessional behavior, including a lack of civility and respect, can have a negative impact on patient safety and quality of care. Methods. We used a focused review in the context of 20 years of experience of assessing, treating, and remediating unprofessional behavior. The review highlights that unprofessional behavior can stem from a variety of sources, including health, psychological/psychiatric issues, social functioning or support, or a combination of these. The review covers the challenges in the work environment and the relationship between outcome, as experienced by the physician, and the likelihood the physician will repeat or modify his or her behavior. Results. Based on the evidence provided in the review and our clinical and research experience, we offer a new framework for the assessment, treatment, and remediation of physicians with professionalism transgressions: the Environmentally Valid Learning Approach. The approach is related to and expands on Miller's Pyramid by adding bio-psycho-social functioning and professional identity to the Pyramid. It emphasizes the dynamic and environmental characteristics of professional identity. Conclusions. Effective intervention is possible. Consideration of contributory factors, addressing/treating those factors, teaching/remediating skill deficiencies, and determining elements that need to be in place to foster implementation and maintenance of the developing skills are necessary components for successful resolution. The behavior is fully remediated when a self-sustaining alternative to the unprofessional behavior is established and the desired behavior becomes a permanent part of the physician's behavioral repertoire. (C) 2020 by The Society of Thoracic Surgeons