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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.

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.

This study aims to explore the emotion-based mediator of information security fatigue in the relationship between employees’ information security–related stress (SRS) and information security policy (ISP) compliance intention and the effects of psychological capital (PsyCap) on relieving SRS and promoting compliance.

The authors tested a series of hypotheses by applying partial least squares–based structural equation modeling to survey data from 488 employees in Chinese enterprises.

The results suggest that the relationship between SRS and ISP compliance intention is fully mediated by information security fatigue. Employees’ SRS promotes their information security fatigue, which reduces their intention to follow ISPs. In addition, employees with high PsyCap may experience low levels of SRS and information security fatigue, which promotes their willingness to comply with ISPs.

This study extends knowledge by introducing information security fatigue and PsyCap to the field of information security management, and it calls attention to the effects on information security behaviors of employee emotions and positive psychological resources in an organization. The authors reveal the emotion-based mediating effect of information security fatigue and the positive influence of PsyCap in information security management.

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.

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.

The purpose of this paper is to examine the influence of transparency on consumers' judgment and decision-making.

This study uses an experimental research design in which participants' negative emotions dynamically change driven by group emotional interactions when they are experiencing a group complaint.

The experimental results show that compared with opaque products, transparent products make consumers rely more on emotions to make judgments and decisions (Experiment 1). It is precise because transparency increases the influence of emotion on consumers' judgment and decision-making that positive emotion makes consumers' evaluation and willingness to pay higher, while negative emotion makes consumers' evaluation and willingness to pay lower (Experiments 2 and 3). Transparency will also affect consumers' subsequent judgment and decision-making methods, so they are more inclined to choose the option with the dominant emotional dimension (Experiment 4).

Previous studies mainly focus on the impact of transparent packaging on consumers and discuss the impact of transparent packaging on consumer product evaluation and consumption quantity. This study proves that product-related transparent elements can also affect consumers' decision-making methods, making them more dependent on emotions to make decisions, enriching the research on the influencing factors of consumer decision-making methods.

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.