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The purpose of this paper is to investigates how organizational learning, absorptive capacity, cultural integration, specialization of the acquired firm and characteristics of transferred knowledge impact innovation performance subsequent to overseas acquisitions.

Survey responses from 222 Chinese multinational enterprises engaged in overseas acquisitions.

Differences between acquiring and acquired firms’ capabilities, while having a positive direct influence, suppress the positive impact of organizational learning and absorptive capacity, suggesting that multinationals require some basic level of capabilities to appropriate value from overseas acquisitions.

This paper investigates the impact of knowledge-seeking overseas acquisition of Chinese multinationals on innovation performance, as this appears to be the primary motive for making such acquisitions.

Knowledge-seeking overseas acquisition should be based upon the absorptive capacity of the acquiring firm and complementarity between both firms. In knowledge-seeking overseas acquisitions, establishing an effective organizational learning mechanism is necessary for improving innovation performance.

This paper reports on the behaviour and innovation performance of Chinese multinationals through analysis of primary data.

This paper examines the innovation behavior of family-owned firms versus non-family-owned firms. The role of internal family governance and the influence of external stimuli (competition) on innovation are also considered.

The data of 20,995 family and non-family firms across 38 countries are derived from the World Bank Enterprise Survey during the period 2019–2020. Probit models are used to examine the impact of family ownership, family governance, and competition on innovation outcomes.

Family firms are more likely to make R&D investments, acquire external knowledge, engage in product innovation (including innovations that are new to the market) and process innovation, relative to non-family firms. However, a high propensity of family member involvement in top management positions can reduce innovation. Competition has a negative impact on innovation outcomes for both family and non-family firms, but it has a positive moderating effect on the innovation activities of family firms where a higher level of family member involvement in management is present.

This paper provides novel insights into family firm innovation dynamics by identifying family firms as more innovative than non-family firms for all types of indicators, debunking the idea that family firms are conservative, reluctant to change, and averse to the risks in innovation activities. However, too much family involvement in decision making may stifle some innovation activities in family firms, except in cases where the operating environment is highly competitive; this provides new insights into the ownership-management dynamic of family firms.

It is an important style classification way to sort out suits according to the button arrangement. However, since the different dressing ways of suit cause the buttons to be easily occluded, the traditional identification methods are difficult to identify the details of suits, and the recognition accuracy is not ideal. The purpose of this paper is to solve the problem of fine-grained classification of suit by button arrangement. Taking men's suits as an example, a method of coordinate position discrimination algorithm combined faster region-based convolutional neural network (R-CNN) algorithm is proposed to achieve accurate batch classification of suit styles under different dressing modes.

The detection algorithm of suit buttons proposed in this paper includes faster R-CNN algorithm and coordinate position discrimination algorithm. Firstly, a small sample base was established, which includes six suit styles in different dressing states. Secondly, buttons and buttonholes in the image were marked, and the image features were extracted by the residual network to identify the object. The anchors regression coordinates in the sample were obtained through convolution, pooling and other operations. Finally, the position coordinate relation of buttons and buttonholes was used to accurately judge and distinguish suit styles under different dressing ways, so as to eliminate the wrong results of direct classification by the network and achieve accurate classification.

The experimental results show that this method could be used to accurately classify suits based on small samples. The recognition accuracy rate reaches 95.42%. It can effectively solve the problem of machine misjudgment of suit style due to the cover of buttons, which provides an effective method for the fine-grained classification of suit style.

A method combining coordinate position discrimination algorithm with convolutional neural network was proposed for the first time to realize the fine-grained classification of suit style. It solves the problem of machine misreading, which is easily caused by buttons occluded in different suits.

The purpose of this study is to exploring the factors influencing renewable energy consumption intentions and behaviors among eco-tourism visitors in Bangladesh, developing theory of sustainable consumption behaviors (TSCB).

Based on review of previous empirical studies and other literatures, and collection of 399 usable responses, the study is conducted through partial least squares structural equation modeling (PLS-SEM) by using Smart PLS3.3.3.

The study results divulge that renewable energy consumption intentions significantly influence renewable energy consumption behavior; and the carbon mitigation norms and energy saving norms significantly impact on renewable energy consumption intentions among eco-tourists in Bangladesh.

The findings imply that availability of renewable energy consumption options may attract tourists towards eco-tourism in Bangladesh.

This study is one of the first attempts to developing the theory of sustainable consumption, exploring the integrated impacts of carbon mitigation norms, energy saving norms and renewable energy consumption intentions on eco-tourists’ renewable energy consumption behaviors in Bangladesh.

The use of nostalgia in the marketing domain has been popular around the world. Nostalgia has been considered a complex yet ambivalent emotion, which has ignited curiosity among marketing researchers and practitioners alike. In response to calls from marketing practitioners and scholars to understand nostalgia formation among consumers, this study tracks the evolution of nostalgia concepts in the domains of marketing and, more generally, business management. This study aims to highlight the development of a theoretical framework to integrate existing concepts and offer implications based on understanding nostalgia as a phenomenon among consumers as a tool for marketing practice.

This study is descriptive and inductive in nature. The manuscript is designed and positioned as a conceptual study exploring nostalgia’s journey from the domain of psychology to business management. The study synthesizes concepts of nostalgia from psychology, sociology and business management.

The study reveals that nostalgia in the business-management domain is not perceived in the same way as in psychology studies. It has journeyed through different schools of thought and is now used as an impactful marketing practice. The manuscript offers relevant information to marketing practitioners to improve their nostalgia marketing strategies, such as advertising and promotions, retro-branding, crowd-sourcing and culturally oriented practice. Subsequently, the manuscript offers pointers for understanding consumers across the generations and exploring nostalgia and consumption patterns for future research.

The manuscript offers relevant information about nostalgia to marketing practitioners to improve their nostalgia marketing strategies and proposes avenues for future research to the domain scholars.

To the best of the authors’ knowledge, there is no comprehensive paper tracking the journey of nostalgia in business practices and providing directions for future research. This study extends existing literature both by suggesting future research directions and by drawing marketing practitioners’ attention to a conceptual framework for understanding the processes of and relationships with consumer nostalgia, including ways to use consumer nostalgia within marketing practices.

The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships.

This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor.

Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain.

It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.