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The purpose of this paper is to target on designing a short-term load prediction framework that can accurately predict the cooling load of office buildings.

A feature selection scheme and stacking ensemble model to fulfill cooling load prediction task was proposed. Firstly, the abnormal data were identified by the data density estimation algorithm. Secondly, the crucial input features were clarified from three aspects (i.e. historical load information, time information and meteorological information). Thirdly, the stacking ensemble model combined long short-term memory network and light gradient boosting machine was utilized to predict the cooling load. Finally, the proposed framework performances by predicting cooling load of office buildings were verified with indicators.

The identified input features can improve the prediction performance. The prediction accuracy of the proposed model is preferable to the existing ones. The stacking ensemble model is robust to weather forecasting errors.

The stacking ensemble model was used to fulfill cooling load prediction task which can overcome the shortcomings of deep learning models. The input features of the model, which are less focused on in most studies, are taken as an important step in this paper.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

The results confirm management innovation as a complex project concerning organizational routines which represent a central and fundamental element of organizations. Also, it finds that organizational routines evolve in innovation implementation through a three-phase process consisting of the existing-routine-domination phase, the new-routine-creation phase, and solidification phase, each exhibiting different innovation activities and characteristics of participants’ cognition and behaviors; recreation of new routines is the key for routine evolution and, thus, for success of management innovations.

The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

The purpose of this paper is to address how management innovations are implemented deeply at the most micro level of organizations, namely, organizational routines, or to investigate the process through which organizational routines evolve in implementing management innovations, with existing routines overturned and new routines created and solidified.

This paper adopts an interpretive and exploratory case study on the case of Day-Definite (DD) innovation which has successfully brought Arima World Group Company Limited (HOAU) into a new value-added arena, in terms of timing, security and high service quality. Considering that DD innovation reflects a systematic innovation of the whole organization, this paper focuses on it to explore the complex implementation mechanism of management innovation. Multiple approaches were utilized during data collection to meet criteria for trustworthiness, including semi-structured interviews, archival data and observation; and the data analysis went through a five-step process.

The results confirm management innovation as a complex project concerning organizational routines which represent a central and fundamental element of organizations. Also, it finds that organizational routines evolve in innovation implementation through a three-phase process consisting of the existing-routine-domination phase, the new-routine-creation phase and -solidification phases, each exhibiting different innovation activities and characteristics of participants’ cognition and behaviors; recreation of new routines is the key for routine evolution, thus for success of management innovations.

This research is constrained by several limitations. The set-up framework of organizational routine evolution in innovation implementation needs a further confirmation in more organizations; other elements, such as cognition of managers, resource orchestration, environmental elements or organizational culture, should be considered for the success of innovation implementation; and more attention should be paid to the potential power asymmetries among participants and its potential influence on forming shared schemata and subsequent new routines, besides interactions and role taking.

The findings offer some valuable insights for further research on management innovation and organizational routines and hold important implications for management practices. This research extends research on management innovation and the Kurt Lewin Change Theory and Change Model to explore innovation implementation at a most micro level; furthers research on organizational routines, especially routine dynamic theory, by holding the two-component view and exploring the process through which organizational routines evolve; and contributes to research on the relationship between organizational routines and innovations by taking an organizational routines’ perspective. It reminds managers of the depth and complication of innovation implementation.

Surrogate-assisted evolutionary algorithms (SAEAs) are the most popular algorithms used to solve design optimization problems of expensive and complex engineering systems. However, it is difficult for fixed surrogate models to maintain their accuracy and efficiency in the face of different issues. Therefore, the selection of an appropriate surrogate model remains a significant challenge. This paper aims to propose a dynamic adaptive hybrid surrogate-assisted particle swarm optimization algorithm (AHSM-PSO) to address this issue.

A dynamic adaptive hybrid selection method (AHSM) is proposed. This method can identify multiple ensemble models formed by integrating different numbers of excellent individual surrogate models. Then, according to the minimum root-mean-square error, the best suitable surrogate model is dynamically selected in each generation and is used to assist PSO.

Experimental studies on commonly used benchmark problems, and two real-world design optimization problems demonstrate that, compared with existing algorithms, the proposed algorithm achieves better performance.

The main contribution of this work is the proposal of a dynamic adaptive hybrid selection method (AHSM). This method uses the advantages of different surrogate models and eliminates the shortcomings of experience selection. Furthermore, the empirical results of the comparison of the proposed algorithm (AHSM-PSO) with existing algorithms on commonly used benchmark problems, and two real-world design optimization problems demonstrate its competitiveness.

This study aims to address the issue of perfunctory sharing that arises in knowledge governance due to a lack of willingness to share knowledge between individuals within the same organization. This knowledge-sharing process does not occur simultaneously for both parties but follows a sequential progression. Additionally, this governance model fully considers the willingness of both parties to share and effectively addresses the two knowledge characteristics that influence their willingness to do so.

This study follows inductive logic and primarily adopts an interpretive case study approach to conduct a longitudinal exploratory case study. An incubator enterprise with active knowledge-sharing activities and significant knowledge governance effects is selected as the research subject. The governance system is explained through the lens of prospect theory at the mechanism level.

In the study of the knowledge-sharing process, the authors observed a new challenge: perfunctory behavior, whereby individuals engage in knowledge-sharing activities that lack substantial effects as a way to avoid genuine sharing. From this, a new knowledge-sharing model was extracted, the cold start and hot feedback model, which follows a sequential (rather than simultaneous) progression. Using the deterministic effect of prospect theory and the principle of reference dependence, the governance mechanism of corporate knowledge sharing was analyzed from the perspective of knowledge-sharing willingness.

Based on prospect theory, this study primarily explains how the governance mechanism influences the willingness to share knowledge from the perspective of four principles. In the future, threat rigidity theory and commitment escalation theory can be combined to further analyze the willingness to share knowledge from the perspectives of pressure and cost. Empirical research methods can also be used to test and enrich the research results of this paper.

After considering the willingness to share knowledge, a new knowledge-sharing model and corresponding knowledge-sharing governance model are proposed, and prospect theory is extended to the knowledge-based theory research field.

This paper aims to explore the effectiveness of customer inoculation strategies in the context of AI service failures in the hospitality and tourism industries. Furthermore, it examines how these strategies can enhance customer complaint behavior and satisfaction with service recovery, thereby improving the overall service experience.

Four distinct studies were conducted: Study 1 investigated the influence of customer inoculation on complaint behavior post-AI service failure. Study 2 assessed the impact of service remedies on customer satisfaction. Study 3 explored the implications of initial purchase and usage intentions. Finally, Study 4 validated the findings using a large-scale online survey.

The results indicated that customer inoculation significantly increases customer complaint behavior and satisfaction with service remedies following AI service failures. They also showed that this relationship is mediated by psychological distance. Furthermore, customer inoculation positively affects initial purchase and usage intentions, demonstrating effectiveness at various customer engagement stages.

This study enriches the literature on AI hospitality service failure and recovery by introducing the novel concept of customer inoculation. Additionally, it significantly contributes to the inoculation theory literature, which covers diverse fields. Practically, this study proposes an efficient and low-cost strategy for marketers.

This study introduces the concept of customer inoculation in the context of AI service failures, a novel approach in the hospitality and tourism literature. It provides empirical evidence of the efficacy of the strategy, bridging a crucial gap in understanding customer behavior in the face of technological disruptions.

This study aims to investigate the impact of technological turbulence on entrepreneurial orientation (EO) in Chinese e-commerce enterprises. It also examines the mediating roles of business ties and political ties, and the moderating effect of transaction uncertainty on these relationships.

A sample of 173 Chinese e-commerce enterprises was analyzed using survey data. Structural equation modeling was employed to test the proposed hypotheses, including the direct effects of technological turbulence on EO, the mediating roles of business and political ties, and the moderating effect of transaction uncertainty.

The results indicate a positive correlation between technological turbulence and EO. Business ties mediate the relationship between technological turbulence and EO, while political ties do not. Transaction uncertainty negatively moderates the relationship between business ties and EO but does not significantly affect the relationship between political ties and EO. Additionally, EO positively impacts market performance.

This study extends the understanding of how external environmental factors, such as technological turbulence, influence EO in the context of Chinese e-commerce. It highlights the differential roles of business and political ties and provides insights into the moderating effects of transaction uncertainty. The findings offer practical implications for e-commerce firms seeking to enhance their entrepreneurial capabilities in turbulent environments.

The purpose of this paper is to empirically analyze the effects of stock price synchronicity and herding behavior of qualified foreign institutional investors (QFII) on stock price crash risk, especially the mediating effect of herding behavior of QFII on the relation of stock price synchronicity and stock price crash risk.

Taking China’s A-share listed companies from 2005 to 2014 and QFII holding shares data as the research sample, this study calculates herding effect index, sock price synchronicity index and stock price crash risk index, and perform linear regression.

This study concludes that, either herding behavior of QFII or the stock price synchronicity can increase the stock price crash risk. Further study reveals that, the herding behavior of QFII also improves the effect of stock price synchronicity on stock price crash risk. Namely, herding behavior of QFII acts as the mediating role between stock price synchronicity and stock price crash risk.

This study empirically analyzes and verifies the mediating roles of herding behavior of QFII in affecting the relation of sock price synchronicity and stock price crash risk for the first time. The findings of this study contribute to the study of the role of QFII in stabilizing Chinese security market.

The study aims to investigate the effects of bodily feelings on preference for robotic service by examining direct and indirect sensations from physical and metaphorically projected bodily feelings.

Through four empirical experiments involving video and recall tasks to metaphorically manipulate participants’ bodily warmth and directly manipulate ambient temperature, the authors explored the mediating role of the need for warmth and the moderating role of robotic features (warmth vs competence) on consumer willingness to engage with and pay for robotic services.

Warmth perception exhibits a positive correlation with robotic services. This relationship is mediated by the need for warmth. Moreover, when customers experience a sensation of physical warmth, they show a greater willingness to pay for a robotic service exhibiting competence versus warmth.

This research contributes to the literature by integrating the feelings-as-information theory and the mind perception view to understand the judgment of robotic services. It extends the application of the embodied cognition theory, highlighting the significance of bodily feelings as a source of information in customer decision-making processes. Furthermore, this research explores the metaphoric influence of service features on bodily responses, providing new insights into the role of embodiment and mental perception in robotic service evaluations.

Managers should consider using different robots based on seasonal settings to meet customers’ need for warmth. Understanding customers’ bodily feelings and the metaphoric influence of service features contributes to the design of more effective and customer-centric robotic services.

This inquiry explores the metaphoric influence of service features on bodily responses, providing new insights into the role of embodiment and mental perception in robotic service evaluations.