Search results

The purpose of this study is to test the impact of time and price sensitivity on consumer satisfaction and purchase intention on online-to-offline (O2O) takeout platforms and explore the moderating effect of purchase preference on time sensitivity and satisfaction, as well as price sensitivity and satisfaction, in order to guide market pricing.

A structural equation model (SEM) of customer purchase intention was constructed, and the relationships between the variables (time sensitivity, price sensitivity, satisfaction and purchase intention) were examined. The completed questionnaires of 349 respondents were collected from the Questionnaire Star platform in China. The research model and hypotheses were then tested. Analytic hierarchy procedure was used to determine the moderating effect of purchase preference. Finally, the study proposes a pricing strategy for customer-active selective services.

Satisfaction positively influences purchase intention, and price sensitivity significantly increases satisfaction and further increases purchase intention; however, time sensitivity negatively affects satisfaction. Specifically, purchase preference has strongly moderated the relationship between time, price sensitivity and satisfaction. In addition, the findings show that when purchase preference is high, the effect of price sensitivity on satisfaction is stronger, suggesting the importance of purchase preference in strengthening purchase intentions. The research work recommends a pricing strategy involving value-added pricing primarily for time-sensitive customers, which can help build a high-end brand image and reduce price competition. Reduced pricing is mainly for price-sensitive customers, which is conducive to stimulating consumption within a specific time. This pricing strategy is important for adjusting market sensitivity and flexibility.

This research provides new ideas for related disciplines and guidance for the differentiated pricing and promotion of takeout platforms, as well as a theoretical basis for the diversified development of takeout platforms, improvement of personalized service quality and enhancement of customer stickiness. This study fills gaps in the existing literature on the moderating effect of purchase preference on time sensitivity and satisfaction and price sensitivity and satisfaction.

The main goal of the article is to determine the mediating role of HRM outcomes in the relationships between staffing the organization and company performance results and to establish whether there are any identifiable regularity in this scope in the pre-pandemic and pandemic period in the HQs and foreign subsidiaries of MNCs.

The empirical research included 200 MNCs headquartered in Central Europe. To capture the actual relations between the variables under study the raw data in the variables were adjusted with the efficiency index (EI). The Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to verify the research hypotheses and assess the mediating effects.

The research findings show that, with the exception of the HQs in the pandemic period, when staffing had a negative effect on the company performance results in quality, in other cases it had a positive effect on results in HRM, finance, innovativeness and quality, both in the pre-pandemic and pandemic period, although this effect was not always statistically significant. Furthermore, the company's performance results in HRM mediate positively the relationships between staffing and the other three categories of company performance results, regardless of the organizational level (HQs' or subsidiaries') and time period under consideration. Additionally, during the pandemic, the company's performance results in HRM mediate the relationships between staffing and the other company's performance results stronger than in the pre-pandemic time.

In addition to confirming the results of some other studies, the article also provides new knowledge. It determines the mediating role of HRM outcomes in the relationship between staffing and company performance results in finance, innovativeness and quality. Moreover, it identifies certain regularities in the four studied contexts, which is a novelty in this type of research. It also uses an innovative approach to including employee KPIs as the efficiency index in analyzing the relationships between the variables under study.

While the three lines model (TLM) provides an organizational structure to execute risk and control duties, research and practice show limitations in the model's implementation. These limitations result in governance issues. Such issues, together with control weaknesses, could be addressed by leveraging properties of distribution, transparency, and immutability of blockchain technology. To this end, in this paper the authors propose a conceptual control framework based on blockchain technology to augment control practice.

The design of the resulting blockchain-based control framework (BBCF) and its prototype, based on the design science research methodology (DSRM), is presented and discussed in terms of the potential impact in the context of the identified problems within the TLM.

One potential outcome of BBCF could be to redefine the scope and boundaries of some of the activities in audit and control practices from a more static to a more dynamic and prospective role. In a larger context of improving governance practices, the BBCF could set the path for a more inclusive and participatory interaction between the different governance actors of an organization.

However, this assumes that blockchain is more widely adopted despite its complexity and rigidity.

BBCF covering both a conceptual model design and a reference implementation provides an innovation in audit and control. BBCF could include all relevant stakeholders who have an interest in corporate governance and control activities, including the regulators.

The contribution intends to serve both as a starting point for discussing the evolution of audit and control practice based on blockchain technology, as well as an initial actionable prototype for experimentation and further development.

This study probes the psychological interplay between investor sentiment and the returns of cryptocurrencies Bitcoin and Ethereum. Employing the Granger causality test, the authors aim to gauge how extensively the Fear and Greed Index (FGI) can predict cryptocurrency return movements, exploring the intricate bond between investor emotions and market behavior.

The authors used the Granger causality test to achieve research objectives. Going beyond conventional linear analysis, the authors applied Smooth Quantile Regression, scrutinizing weekly data from July 2022 to June 2023 for Bitcoin and Ethereum. The study focus was to determine if the FGI, an indicator of investor sentiment, predicts shifts in cryptocurrency returns.

The study findings underscore the profound psychological sway within cryptocurrency markets. The FGI notably predicts the returns of Bitcoin and Ethereum, underscoring the lasting connection between investor emotions and market behavior. An intriguing feedback loop between the FGI and cryptocurrency returns was identified, accentuating emotions' persistent role in shaping market dynamics. While associations between sentiment and returns were observed at specific lag periods, the nonlinear Granger causality test didn't statistically support nonlinear causality. This suggests linear interactions predominantly govern variable relationships. Cointegration tests highlighted a stable, enduring link between the returns of Bitcoin, Ethereum and the FGI over the long term.

Despite valuable insights, it's crucial to acknowledge our nonlinear analysis's sensitivity to methodological choices. Specifics of time series data and the chosen time frame may have influenced outcomes. Additionally, direct exploration of macroeconomic and geopolitical factors was absent, signaling opportunities for future research.

This study enriches theoretical understanding by illuminating causal dynamics between investor sentiment and cryptocurrency returns. Its significance lies in spotlighting the pivotal role of investor sentiment in shaping cryptocurrency market behavior. It emphasizes the importance of considering this factor when navigating investment decisions in a highly volatile, dynamic market environment.

The purpose of this paper is to understand the post-COVID-19 fluctuations in the building construction demand from various angles at the national, regional, and sectoral levels. Despite the significant impact of COVID-19 on the building construction industry, a detailed quantitative analysis of the COVID-19 impact on the building construction demand is still lacking. The current study aims to (1) establish a statistical approach to quantify the COVID-19 impact on the building construction demand; (2) investigate the post-COVID-19 fluctuations in the construction demand of different building services, regional markets, and building sectors using the historical time series of the architecture billings index (ABI); and (3) identify vulnerable market and sector and discuss the post-COVID-19 recovery strategies.

The research methodology follows four steps: (1) collecting national, regional, and sectoral ABIs; (2) creating seasonal autoregressive integrated moving average models; (3) illustrating cumulative sum control charts to identify significant ABI deviations; and (4) quantifying the post-COVID-19 ABI fluctuations.

The results show that all the ABIs experienced a statistically significant decrease after COVID-19. The project inquiries index reduced more but recovered faster than billings and design contracts indices. The midwest billings index decreased the most among the regional ABIs and the commercial/industrial billing index dropped the most among the sectoral ABIs.

This study is unique in the way that it utilized the ABI data and the approach using SARIMA models and CUSUM control charts to assess the post-COVID-19 building construction demand represented by ABI fluctuations.

Overcrowding continues to afflict emergency departments (EDs), and its attendant consequences are becoming increasingly severe. The burden of the COVID-19 pandemic is further escalating the situation worldwide. One of the most critical questions is how to adequately quantify what constitutes overcrowding and determine implications for operations management in improving service efficiency. This paper aims to discuss the aforementioned.

The authors propose the time and class complexity measures for ED service systems, taking into account important patient-level and system characteristics. Using an extensive data set from a Canadian ED, the authors investigate the performance of complexity-based measures in predicting service delays.

The authors find that the complexity measure is potentially more important than some well-known crowding metrics. In particular, EDs can improve service efficiency by managing the level of complexity within a desirable interval. Furthermore, complexity exposes how the interplay between demand-side behavioral changes and supply-side responses affects operational performance. Moreover, the results suggest that arrival patterns—the number of patients of each class arriving per time and times between events (arrivals and service completions)—increase the risk of service delays more than the demand volume.

This paper is the first to provide an extensive investigation into the application of the complexity-based measure for ED crowding. The study demonstrates potential values to be gained in ED service systems if complexity measure is incorporated into their operations management decisions.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

This study aims to present and test a model of small business owners’ adaptation during the COVID-19 pandemic, through the lenses of the resource-based view of the firm, upper echelon theory (UET) and positive psychology. Specifically, it examined the relationships between personal characteristics and strategic pivot behavior in a sample of small business owners during a peak period of the crisis in Israel.

A sample (N = 202) of small business owners provided information on their business and demographics, and responded to questionnaires assessing their personality (Big Five personality traits), emotional intelligence (EI) and reported the extent to which they implemented strategic changes during the pandemic. These changes were categorized as “positive” (e.g. shifting to new markets, adding partners or investors) or “negative’ (e.g. letting employees go).

The results partially support an association between personality traits and “positive” strategic change behavior, mediated by EI.

The results suggest that business owners' personality traits, and chief among them – EI may play a key role in enabling flexibility when dealing with a long-term crisis or threat. However, market and legislative differences between markets may limit the generalizability to other sectors or countries.

To the best of the authors’ knowledge, this study is among the first to address small businesses’ COVID-19-related challenges from a personal-resource perspective by applying a theoretical lens integrating the RBV of the firm, UET and positive psychology. The findings provide a better understanding of the ways in which business owners’ personal resources account for business pivot behavior in times of crisis.

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.