Xianming Wu, Nathaniel C. Lupton and Yuping Du
The purpose of this paper is to investigates how organizational learning, absorptive capacity, cultural integration, specialization of the acquired firm and characteristics of…
Abstract
Purpose
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.
Design/methodology/approach
Survey responses from 222 Chinese multinational enterprises engaged in overseas acquisitions.
Findings
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.
Research limitations/implications
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.
Practical implications
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.
Originality/value
This paper reports on the behaviour and innovation performance of Chinese multinationals through analysis of primary data.
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Yuping Yin, Frank Crowley, Justin Doran, Jun Du and Mari O'Connor
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Yanwen Yang, Yuping Jiang, Qingqi Zhang, Fengyuan Zou and Lei Du
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Imene Guermazi, Aida Smaoui and Mohamed Chabchoub
This paper focuses on the commitment of a leading Middle Eastern country – Saudi Arabia – to the United Nations (UN) Sustainable Development Goals (SDGs), particularly SDG13…
Abstract
Purpose
This paper focuses on the commitment of a leading Middle Eastern country – Saudi Arabia – to the United Nations (UN) Sustainable Development Goals (SDGs), particularly SDG13, climate preservation. This paper aims to investigate the determinants of greenhouse gas emissions by examining their correlation with economic growth, population growth, renewable energies, forest area, digitalization and monetary policy.
Design/methodology/approach
This research observes greenhouse gas (GHG) emissions and the potential influencing factors during 1990–2023. It employs the autoregressive distributed lag model (ARDL) after testing the stationarity of the variables.
Findings
The findings show that population growth, gross domestic product (GDP) growth, percentage of individuals using the internet and forest rents are significant determinants of carbon oxide (CO2) emissions. Further, methane (CH4) emissions are significantly associated with population growth, GDP growth, percentage of individuals using the internet and renewable internal freshwater resources. Nitrous oxide (N2O) emissions depend significantly on the percentage of individuals using the internet and renewable internal freshwater resources.
Practical implications
This research helps policymakers in Saudi Arabia and worldwide identify the factors moderating GHG emissions, and accordingly design targeted interventions. These initiatives would substantially reduce GHG and further global climate goals. Additionally, focusing on Saudi Arabia, a significant emerging country in the Middle East, has broader implications. The findings offer insights that extend beyond its borders, providing valuable lessons for governments in the Middle East and worldwide to assess and improve their initiatives toward SDG13. Therefore, monitoring greenhouse gas emissions in this key country boosts global progress toward the UN’s 2030 Agenda for Sustainable Development. Furthermore, this paper aligns with the Principles for Responsible Management Education (PRME) by leveraging academic and managerial strategies toward sustainability and climate action initiatives.
Originality/value
This study adds to the limited literature on the determinants of GHG emissions in the Middle Eastern region, particularly in Saudi Arabia. In addition to CO2, it also focuses on CH4 and N2O emissions. It shows the beneficial effect of renewable internal freshwater resources. It uses the ARDL model to distinguish between the short- and long-run associations.
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The purpose of this study is to exploring the factors influencing renewable energy consumption intentions and behaviors among eco-tourism visitors in Bangladesh, developing…
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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.
Practical implications
The findings imply that availability of renewable energy consumption options may attract tourists towards eco-tourism in Bangladesh.
Originality/value
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.
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Sudhir Rana, Sachin Kumar Raut, Sanjeev Prashar and Majdi Anwar Quttainah
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Originality/value
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.
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Haosen Liu, Youwei Wang, Xiabing Zhou, Zhengzheng Lou and Yangdong Ye
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.