Search results

The purpose of this study is to analyze the systematic relationships among dynamic capabilities in startups’ survival.

This study is based on a systematic literature review on dynamic capabilities related to startups’ survival, following the content analysis approach.

This study presents four different perspectives of analysis about dynamic capabilities from resources exchange and business factors that meet needs of startups' survival. It also points out new area for future research in this field. In doing so, this study differentiates itself by its approach not limiting dynamic capabilities research and enriching entrepreneurs' capability theory.

By indicating an evolution of dynamic capabilities theory among tangible and intangible resources exchange in a more favorable adaptation to startups growth, this study boosters and contributes to the society, economy in general and to the science of business management in various perspectives such as overcoming cognitive barriers, entrepreneur’s commitment, innovation capabilities and knowledge capacity of startups.

This study amplifies dynamic capabilities vision in startups’ survival as one of the main sources for growth in this type of organizations. It also develops a deeper understanding about new avenues for dynamic capabilities theory among tangible and intangible resources exchange.

The present study is intended to develop an effective approach to the real-time modeling of general dynamic nonlinear systems based on the multidimensional Taylor network (MTN).

The authors present a detailed explanation for modeling the general discrete nonlinear dynamic system by the MTN. The weight coefficients of the network can be obtained by sampling data learning. Specifically, the least square (LS) method is adopted herein due to its desirable real-time performance and robustness.

Compared with the existing mainstream nonlinear time series analysis methods, the least square method-based multidimensional Taylor network (LSMTN) features its more desirable prediction accuracy and real-time performance. Model metric results confirm the satisfaction of modeling and identification for the generalized nonlinear system. In addition, the MTN is of simpler structure and lower computational complexity than neural networks.

Once models of general nonlinear dynamical systems are formulated based on MTNs and their weight coefficients are identified using the data from the systems of ecosystems, society, organizations, businesses or human behavior, the forecasting, optimizing and controlling of the systems can be further studied by means of the MTN analytical models.

MTNs can be used as controllers, identifiers, filters, predictors, compensators and equation solvers (solving nonlinear differential equations or approximating nonlinear functions) of the systems of ecosystems, society, organizations, businesses or human behavior.

The operating efficiency and benefits of social systems can be prominently enhanced, and their operating costs can be significantly reduced.

Nonlinear systems are typically impacted by a variety of factors, which makes it a challenge to build correct mathematical models for various tasks. As a result, existing modeling approaches necessitate a large number of limitations as preconditions, severely limiting their applicability. The proposed MTN methodology is believed to contribute much to the data-based modeling and identification of the general nonlinear dynamical system with no need for its prior knowledge.

The purpose of this paper is to examine the impact of inter-group conflicts on the trust toward the acquirer and then further on cooperation intention after acquisitions in cross-border mergers and acquisitions (M&As), in the lens of the social classification theory. Two types of conflicts (realistic conflicts and symbolic conflicts) and a mediating mechanism (trust toward the acquirer) exhibit different effects on cooperation intention. The research further examines two moderating coping strategies (localizing management and assigning trustworthy expatriate managers) and tests their effectiveness in promoting trust toward the acquirer and increasing cooperation intention in cross-border M&As.

The research hypotheses were empirically tested in the context of post-acquisition of Chinese companies' cross-border M&As. In total, 600 questionnaires were provided to the research participants of 37 acquired firms/units from advanced economies of 12 Chinese companies, and 209 valid questionnaires were collected (the response rate is 34.83%). Confirmatory factor analysis was conducted to verify data validity and hierarchical multiple regression analyses were employed to test relational and moderating effects.

This research demonstrates that both realistic and symbolic conflicts can reduce the intention to cooperate, but the latter does not have a significant influence. The results also uncover that whether employees from acquired firms trust in their acquirers mediates the relationship between realistic conflicts and cooperation intention. Moreover, management localization (a measurement of whether local/original managers are retained with a high degree of freedom and autonomy after M&As) and trustworthiness of expatriate managers (a measurement of whether the assigned expatriate managers is trustworthy) positively moderate the relation between realistic conflicts and trust toward the acquirer. In addition, trust toward the acquirer mediates the interaction of realistic conflicts and management localization on the cooperation intention of the acquirers' employees.

This study examines how inter-organizational conflicts influence trust toward the acquirer and then cooperation intentions in the context of Chinese companies' M&A behavior of targets from developed countries and gain supportive evidence, which enriches the literature on the management of international M&As. By introducing two management localization and trustworthiness of expatriate managers into the model, the research deepens our knowledge of how to build trust toward the acquirer in cross-border M&As.

The purpose of this paper is to explore the influence of a physician’s prosocial behavior on a patient's choices in the online health community (OHC) context. Moreover, the authors explore how such effects differ across different online word-of-mouth (WOM) and professional titles.

Guided by the motivation, opportunity and ability (MOA) framework, this paper develops hypotheses and an econometric model. Then this paper used spline regression to test hypotheses on 6,204 physicians at The Good Doctor (www.Haodf.com), which is one of the largest Chinese OHCs. The authors conducted the propensity score matching and difference-in-difference method (PSM-DID) to address the concern about the bias caused by possible endogeneity concerns.

The authors’ results show that a physician’s prosocial behavior improves a patient's choice only when the strength of a physician’s prosocial behavior is below the tipping point. In addition, the influence of a physician’s prosocial behavior is heterogeneous for physicians with different online WOM and professional titles. For physicians with higher online WOM, the effect of a physician's prosocial behaviors on a patient's choice is positive, while for physicians with lower online WOM, a physician’s prosocial behavior has no impact on a patient’s choice. For physicians with higher professional titles, the quantity of a physician’s prosocial behavior has a positive impact on a patient’s choice, while for physicians with lower professional titles the quality of a physician’s prosocial behavior has a positive impact on a patient’s choice.

This study contributes new knowledge and provides new perspectives to study a patient's choice by addressing the importance of physician's prosocial behavior. With the effort of explicitly explaining the complex mechanisms, this study encourages physicians' engagement in a physician’s prosocial behavior and gives some implications on how to perform the behaviors strategically.

Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However, the classic approach to estimating such parameters is perceived to be imprecise. Herein, the essential features and performances of the ant colony, bee colony and elephant herd optimisation approaches are introduced to the experimental chemist and chemical engineer engaged in adsorption research for aqueous systems. Key research and development directions, believed to harness these algorithms for real-scale water treatment (which falls within the wide-ranging coverage of the Sustainable Development Goal 6 (SDG 6) ‘Clean Water and Sanitation for All’), are also proposed. The ant colony, bee colony and elephant herd optimisations have higher precision and accuracy, and are particularly efficient in finding the global optimum solution. It is hoped that the discussions can stimulate both the experimental chemist and chemical engineer to delineate the progress achieved so far and collaborate further to devise strategies for integrating these intelligent optimisations in the design and operation of real multicomponent multi-complexity adsorption systems for water purification.

The purpose of this study is to investigate whether the corporate environmental, social and governance (ESG) performance of enterprise is influenced by the enterprise digital transformation. In addition, this study explains how enterprise digital transformation affects ESG performance.

The sample covers 4,646 nonfinancial companies listed on China’s A-share market from 2009 to 2021. The study adopts the fixed-effects multiple linear regression to perform the data analysis.

The study finds that enterprise digital transformation has a significant inverted U-shaped impact on ESG performance. Moderate digital transformation can improve enterprise ESG performance, whereas excessive digital transformation will bring new organizational conflicts and increase enterprise costs, which is detrimental to ESG performance. This inverted U-shaped effect is more pronounced in industrial cities, manufacturing industries and enterprises with less financing constraints and executives with financial backgrounds. Enterprise digital transformation mainly affects ESG performance by affecting the level of internal information communication and disclosure, the level of internal control and the principal-agent cost.

The government should take multiple measures to encourage enterprises to choose appropriate digital transformation based on their own production behaviors and development strategies, encourage them to innovate and upgrade their organizational management and development models in conjunction with digital transformation and guide them to use digital technology to improve ESG performance.

This study shows that irrational digital transformation cannot effectively improve the ESG performance of enterprises and promote the sustainable development of the country. Enterprises should carry out reasonable digital transformation according to their own development needs and finally improve the green and sustainable development ability of enterprises and promote the sustainable development of society.

This study examines the relationship between enterprise digital transformation and ESG performance. Different from the linear relationship between the two in previous major studies, this study proves the inverse U-shaped relationship between enterprise digital transformation and ESG performance through mathematical theoretical model derivation and empirical test. This study also explores in detail how corporate digital transformation affects ESG performance, as well as discusses heterogeneity at the city, industry and firm levels. It is proposed that enterprises should take into account their own characteristics and carry out reasonable digital transformation according to their development needs.

With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

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.

Past research often relies on linear relationship assumptions from the perspective of managers when studying the relationship between attribute performance and satisfaction. However, this study extracts tourists’ online reviews to explore asymmetric relationships and identifies island tourism satisfiers, hybrids and dissatisfiers.

The research uses 3,523 reviews from Tripadvisor to examine Langkawi Island’s tourist satisfaction. Latent Dirichlet allocation (LDA) machine-learning approach, penalty–reward contrast analysis and asymmetric impact-performance analysis (AIPA) were employed to extract and analyse the data.

Langkawi’s dissatisfiers included “hotel and restaurant”, “beach leisure”, “water sport”, “snorkelling”, “commanding view”, “waterfall”, “sky bridge walk”, “animal show”, “animal feeding”, “history culture”, “village activity” and “duty-free mall”. Amongst these, five were low performers. Hybrids encompassed “ticket purchasing”, “amenity” “traditional food market” and “gift and souvenir”, all of which were low performers. Only one attribute was categorised as a satisfier: “nature view” which performed exceptionally well.

This study provides recommendations to enhance tourist satisfaction and address tourist dissatisfaction. The elements requiring immediate attention for enhancement are the five low-performance dissatisfiers, as they represent tourists’ fundamental expectations. Conversely, the satisfier or excitement factor (i.e. nature views – mangroves and wildlife) could be prominently featured in promotional materials.

This research constitutes an early endeavour to categorise attributes of island tourism into groups of satisfaction, hybrid or dissatisfaction based on user-generated data. It is underpinned by two-factor and three-factor theories.

The study aims to explore how fashion visual symbols influence consumers' inclination for repurchasing. It attempts to investigate the intricate interplay among three essential variables (social presence, collective excitement and cultural identification) from the perspective of Interaction Ritual Chains theory. Meanwhile, an attempt is made to reveal the underlying patterns in these relationships, fully harnessing the positive impact of fashion brand visual symbols in brand marketing.

This study employs a quantitative research methodology, administering an online survey in China, from which 381 valid responses were collected by simple random sampling. The acquired data were subjected to structural equation model and hypotheses testing.

The analysis reveals that heightened visual symbol perception significantly strengthens consumers' social presence, consequently elevating the probability of collective excitement. This establishes a mediated chain model, reinforcing repurchase intention. Additionally, the moderation effect analysis indicates that cultural identification negatively moderates both direct paths in the mediated chain model, with particularly pronounced effects for low cultural identification.

This study establishes a closed-loop system in fashion brand product marketing, continuously enhancing the intimacy and interactive willingness between consumers, as well as between consumers and the brand. The objective is to increase brand repurchase rates. Additionally, the research provides valuable recommendations and strategies for fashion brands to adapt to Chinese consumer demands, strengthen emotional attachment between consumers and the brand, and achieve sustainable development in the realm of fashion consumption.