Search results

This study aims to examine the resilience of Peruvian startups during the COVID-19 pandemic using a framework proposed by Lengnick-Hall et al. (2011), in which resilience impacts organizational strengthening. The goal is to identify those characteristics that allowed certain startups to discover growth opportunities amid this crisis.

This study analyzed human, social and entrepreneurial capital variables in Peruvian startups using data from a survey conducted in July 2020. Binary logistic regression was used to determine which organizational resources increased the probability of identifying growth opportunities during the pandemic.

The findings suggest that human capabilities become secondary in extreme crises such as pandemics. Critical factors for startup resilience include commercial partnerships with established firms, founders’ capital investment, business maturity and adoption of advanced digital technologies.

This research provides unique insights into startup resilience and growth in Peru during the COVID-19 crisis. The authors observed that business growth during this period was largely unpredictable, with less emphasis on human capabilities. The study highlights the importance of external factors in resilience, the role of collaboration between established firms, the integration of advanced digital technologies and the influence of founders’ investments and business maturity in navigating difficult times.

Este estudio examina la resiliencia de las startups peruanas durante la pandemia de COVID-19 utilizando un marco propuesto por Lengnick-Hall et al. (2011), en el que la resiliencia tiene un efecto en el fortalecimiento de las organizaciones. Su objetivo es identificar las características que permitieron a ciertas startups descubrir oportunidades de crecimiento en medio de esta crisis.

Analizamos variables de capital humano, social y empresarial en startups peruanas utilizando datos de una encuesta realizada en julio de 2020. Se utilizó regresión logística binaria para determinar qué recursos organizativos incrementaban la probabilidad de identificar oportunidades de crecimiento durante la pandemia.

Nuestros hallazgos sugieren que las capacidades humanas pasan a un segundo plano en crisis extremas como las pandemias. Los factores críticos para la resiliencia de las startups incluyen las asociaciones comerciales con empresas establecidas, la inversión de capital de los fundadores, la madurez empresarial y la adopción de tecnologías digitales avanzadas.

Esta investigación proporciona una visión única sobre la resiliencia y el crecimiento de las startups en Perú durante la crisis COVID-19. Observamos que el crecimiento empresarial durante este período fue en gran medida impredecible, con menos énfasis en las capacidades humanas. El estudio subraya la importancia de los factores externos en la resiliencia, el papel de la colaboración con las empresas establecidas, la integración de tecnologías digitales avanzadas, la influencia de las inversiones de los fundadores y la madurez empresarial para navegar en tiempos difíciles.

Este estudo examina a resiliência das startups peruanas durante a pandemia da COVID-19 usando uma abordagem proposta por Lengnick-Hall et al. (2011), na qual a resiliência tem um efeito fortalecedor nas organizações. Seu objetivo é identificar as características que permitiram que determinadas startups descobrissem oportunidades de crescimento em meio a essa crise.

Analisamos variáveis de capital humano, social e empresarial em start-ups peruanas usando dados de uma pesquisa realizada em julho de 2020. A regressão logística binária foi usada para determinar quais recursos organizacionais aumentaram a probabilidade de identificar oportunidades de crescimento durante a pandemia.

Nossas análises sugerem que as capacidades humanas se tornam secundárias em crises extremas, como as pandemias. Os fatores essenciais para a resiliência das startups incluem parcerias comerciais com empresas estabelecidas, investimento de capital dos fundadores, maturidade dos negócios e adoção de tecnologias digitais avançadas.

Esta pesquisa fornece informações exclusivas sobre a resiliência e o crescimento de startups no Peru durante a crise da COVID-19. Observamos que o crescimento das empresas durante esse período foi amplamente imprevisível, com menos ênfase nas capacidades humanas. O estudo destaca a importância de fatores externos na resiliência, o papel da colaboração com empresas estabelecidas, a integração de tecnologias digitais avançadas e a influência dos investimentos dos fundadores e da maturidade dos negócios na superação de tempos difíceis.

The purpose of this paper is to examine the adoption of Islamic finance products (murabaha, musharakah, mudarabah, salam, ijara, istisna and Qard Hassan) by small and medium-sized enterprises (SMEs) in Cameroon, a non-Islamic Sub-Saharan African country.

It used primary data collected from a cross-section of 1,358 SMEs in eight regions of Cameroon using self-administered structured questionnaires. To facilitate the analyses and interpretation, these products are grouped into four groups based on certain characteristics. A multivariate probit model is estimated to take into account the interaction between these different Islamic finance products.

This study revealed that the desire to comply with Sharia law, awareness, attitude and intention were critical determinants of the decision to adopt Islamic finance products by Cameroonian SMEs. The least influential factors were perceived behavioral control, subjective norms, enterprise characteristics (size, age and location) and socio-demographic characteristics of the entrepreneur (gender, age and marital status). The extension of the multivariate approach permitted us to compute for predicted probabilities which revealed that there exists a synergy effect between the different Islamic finance products. That is, Cameroonian SMEs combine different Islamic finance products at the same time based on their needs. This is especially the case between the partnership-based products (musharakah and mudarabah) and manufacture/rent products (istisna and ijara).

Policymakers are encouraged to develop stakeholder-oriented strategies to promote effective consumer education in Islamic finance products which will boost awareness. Also, Islamic finance institutions should endeavor to develop innovative financial products that are Sharia-compliant and economically beneficial to the individual and business needs of SMEs. Moreover, policymakers and management of Islamic finance institutions should ensure the putting in place of effective governance structures to guide Islamic finance operations. Finally, policymakers should endeavor to take into account the possible synergy between the different Islamic finance products in their quest to develop this activity.

To the best of the authors’ knowledge, this is the first study that analyses the adoption of different Islamic finance products while taking into account the possible synergy that exists between these products.

This study was conducted to address the methodical shortcomings and high associated cost of understanding the use of new, poorly understood architectural spaces, such as makerspaces. The proposed quantified method of enhancing current post-occupancy evaluation (POE) practices aims to provide architects, engineers and building professionals with accessible and intuitive data that can be used to conduct comparative studies of spatial changes, understand changes over time (such as those resulting from COVID-19) and verify design intentions after construction through a quantified post-occupancy evaluation.

In this study, we demonstrate the use of ultra-wideband (UWB) technology to gather, analyze and visualize quantified data showing interactions between people, spaces and objects. The experiment was conducted in a makerspace over a four-day hackathon event with a team of four actively tracked participants.

The study shows that by moving beyond simply counting people in a space, a more nuanced pattern of interactions can be discovered, documented and analyzed. The ability to automatically visualize findings intuitively in 3D aids architects and visual thinkers to easily grasp the essence of interactions with minimal effort.

By providing a method for better understanding the spatial and temporal interactions between people, objects and spaces, our approach provides valuable feedback in POE. Specifically, our approach aids practitioners in comparing spaces, verifying design intent and speeding up knowledge building when developing new architectural spaces, such as makerspaces.

Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).

Drawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.

Three numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.

By employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.

This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector.

From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure.

The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible.

Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

The expansion of bike-sharing enterprises has led to a series of social problems, and due to the high breakage rate and high recycling cost of bike sharing, enterprises are reluctant to maintain them. Therefore, government regulation does play a leading role in maintaining bike sharing. This study’s purpose is to investigate how the government should regulate the bike-sharing enterprises that maintain bicycles.

Firstly, the authors assume that there is only one bike-sharing enterprise and establish a game model that the government regulates the enterprise. Furthermore, the authors extend the model to the case that there are two competing enterprises in the market. Finally, through numerical analysis, the influence of various factors on the government strategy and revenue is analyzed.

The authors find that the regulatory probability of two enterprises are regulated by the government is larger than that of one enterprise. When two bike-sharing enterprises compete without government regulation, both will choose the non-self-discipline strategy, thus falling into the Prisoner's Dilemma. If the government regulates them, then both enterprises will choose the self-discipline strategy. Finally, through numerical analysis, it is found that the self-discipline behavior of bike-sharing enterprises is related to the government regulatory probability, the cost of self-discipline, the probability of being reported and the penalties. Interestingly, the cost of government regulation will not affect the regulation probability of government.

This research provides a theoretical reference for the government to make the regulation strategies of bike sharing and achieve the sustainable development of bike sharing.

This research provides a theoretical reference for the government to make the regulation strategies of bike sharing and achieve the sustainable development of bike sharing.

The purpose of this paper is to extract electrochemical reaction kinetics parameters, such as Tafel slope, exchange current density and equilibrium potential, which cannot be directly measured, this study aims to propose an improved particle swarm optimization (PSO) algorithm.

In traditional PSO algorithms, each particle’s historical optimal solution is compared with the global optimal solution in each iteration step, and the optimal solution is replaced with a certain probability to achieve the goal of jumping out of the local optimum. However, this will to some extent undermine the (true) optimal solution. In view of this, this study has improved the traditional algorithm: at each iteration of each particle, the historical optimal solution is not compared with the global optimal solution. Instead, after all particles have iterated, the optimal solution is selected and compared with the global optimal solution and then the optimal solution is replaced with a certain probability. This to some extent protects the global optimal solution.

The polarization curve plotted by this equation is in good agreement with the experimental values, which demonstrates the reliability of this algorithm and provides a new method for measuring electrochemical parameters.

This study has improved the traditional method, which has high accuracy and can provide great support for corrosion research.

With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.

This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.

Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.

This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

This paper investigates how gender, ethnicity, and network membership interact to influence how small and medium-sized enterprise (SME) owner-managers become aware of finance support programmes developed by government policy and/or support schemes advanced by the banking industry.

Drawing on expectation states theory (EST), we develop eight sets of hypotheses and employ the UK SME Finance Monitor data to test them using bivariate probit regression analysis.

In general, network membership increases awareness, but more so for government programmes. We also find no differences between female and male owner-managers when in networks. However, we identify in-network and out-network differences by ethnicity, with minority females seemingly better off than minority males.

Business networks are better for disseminating government programmes than industry-led programmes. For native White women, network membership can enhance policy awareness advantage further, whilst for minorities, networks significantly offset the big policy awareness deficits minorities inherently face. However, policy and practice need to address intersectional inequalities that remain in access to networks themselves, information access within networks, and the significant out-network deficits in awareness of support programmes afflicting minorities.

This study provides one of the first large-scale empirical examinations of intersectional mechanisms in awareness of government and industry-led enterprise programmes. Our novel and nuanced findings advance our understanding of the ways in which gender and ethnicity interact with network dynamics in entrepreneurship.

The study focuses on drug safety regulation capture, reveals the inner mechanism and evolutionary characteristics of drug safety regulation capture and provides suggestions for effective regulation by pharmacovigilance.

The article introduces prospect theory into the game strategy analysis of drug safety events, constructs a benefit perception matrix based on psychological perception and analyzes the risk selection strategies and constraints on stable outcomes for both drug companies and drug regulatory authorities. Moreover, simulation was used to analyze the choice of results of different parameters on the game strategy.

The results found that the system does not have a stable equilibrium strategy under the role of cognitive psychology. The risk transfer coefficient, penalty cost, risk loss, regulatory benefit, regulatory success probability and risk discount coefficient directly acted in the direction of system evolution toward the system stable strategy. There is a critical effect on the behavioral strategies of drug manufacturers and drug supervisors, which exceeds a certain intensity before the behavioral strategies in repeated games tend to stabilize.

In this article, the authors constructed the perceived benefit matrix through the prospect value function to analyze the behavioral evolution game strategies of drug companies and FDA in the regulatory process, and to evaluate the evolution law of each factor.