Dewen Liu, Ying Zou, Peng Lv and Shanji Yao
While the impact of digitalization on businesses has been extensively studied, the influence of digitalization on marketing outcomes in private enterprises has not received…
Abstract
Purpose
While the impact of digitalization on businesses has been extensively studied, the influence of digitalization on marketing outcomes in private enterprises has not received sufficient attention. The current study aims to examine how and when digitalization affects international marketing decisions in the context of private enterprises.
Design/methodology/approach
This study employs data from a survey of Chinese private enterprises conducted in 2020, which constitutes the world's largest dataset of its kind. Nearly 19,000 samples were included in the study. Additionally, we also incorporate supplementary data on digitalization in the Chinese region. Employing various methods, this study empirically and robustly examines the proposed research framework within the context of Chinese private enterprises.
Findings
Based on the resource-based view and agency theory, this paper found that digitalization can positively impact private enterprises’ direct and indirect international marketing decisions. Furthermore, we introduce the inclusion of innovation capacity and board governance as moderators in the model and find that board governance attenuates the influence of digitalization on international marketing decisions, while innovation capacity enhances the impact of digitalization on direct international marketing but diminishes its effect on indirect international marketing.
Originality/value
This study advances the understanding of the impact of digitalization on international marketing in private enterprises, thereby addressing the gap in the limited focus on digitalization in private enterprises. It also demonstrates how private enterprises effectively utilize digitalization to gain marketing advantages in the international market.
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Based on upper echelons theory, this study aims to explore the impact of senior management’s academic experience on corporate risk-taking and analyze the pathways and potential…
Abstract
Purpose
Based on upper echelons theory, this study aims to explore the impact of senior management’s academic experience on corporate risk-taking and analyze the pathways and potential moderating effects of this relationship.
Design/methodology/approach
This study uses panel data of Chinese A-share listed companies in the Shenzhen Stock Exchange and Shanghai Stock Exchange from 2008 to 2020. An ordinary least squares model is used to test the hypothesis.
Findings
The results indicate that senior management’s academic experience suppresses corporate risk-taking, with investment level and cash reserves being two important channels. The moderation effect test shows that the inhibitory effect becomes more pronounced when senior managers with academic backgrounds occupy chief executive officer or chief financial officer roles. Conversely, when academic executives possess overseas/financial backgrounds or increase their compensation incentives, the strength of this disincentive effect diminishes. Moreover, our extended research finds that this inhibitory effect is more pronounced in state-owned companies and those within a strong Confucian cultural environment. Additionally, senior management’s academic experience positively correlates with both current and future market returns and company value.
Originality/value
This study contributes to the development of top management team building and corporate governance practices. Additionally, it furnishes investors with valuable insights into assessing the risk level of companies through the characteristics of their top management teams, thereby facilitating informed investment decision-making and improving capital market resource allocation efficiency.
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Waqar Ahmed Khan, S.H. Chung, Muhammad Usman Awan and Xin Wen
The purpose of this paper is to conduct a comprehensive review of the noteworthy contributions made in the area of the Feedforward neural network (FNN) to improve its…
Abstract
Purpose
The purpose of this paper is to conduct a comprehensive review of the noteworthy contributions made in the area of the Feedforward neural network (FNN) to improve its generalization performance and convergence rate (learning speed); to identify new research directions that will help researchers to design new, simple and efficient algorithms and users to implement optimal designed FNNs for solving complex problems; and to explore the wide applications of the reviewed FNN algorithms in solving real-world management, engineering and health sciences problems and demonstrate the advantages of these algorithms in enhancing decision making for practical operations.
Design/methodology/approach
The FNN has gained much popularity during the last three decades. Therefore, the authors have focused on algorithms proposed during the last three decades. The selected databases were searched with popular keywords: “generalization performance,” “learning rate,” “overfitting” and “fixed and cascade architecture.” Combinations of the keywords were also used to get more relevant results. Duplicated articles in the databases, non-English language, and matched keywords but out of scope, were discarded.
Findings
The authors studied a total of 80 articles and classified them into six categories according to the nature of the algorithms proposed in these articles which aimed at improving the generalization performance and convergence rate of FNNs. To review and discuss all the six categories would result in the paper being too long. Therefore, the authors further divided the six categories into two parts (i.e. Part I and Part II). The current paper, Part I, investigates two categories that focus on learning algorithms (i.e. gradient learning algorithms for network training and gradient-free learning algorithms). Furthermore, the remaining four categories which mainly explore optimization techniques are reviewed in Part II (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks and metaheuristic search algorithms). For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part II): Neural networks optimization techniques and applications” is referred to as Part II. This results in a division of 80 articles into 38 and 42 for Part I and Part II, respectively. After discussing the FNN algorithms with their technical merits and limitations, along with real-world management, engineering and health sciences applications for each individual category, the authors suggest seven (three in Part I and other four in Part II) new future directions which can contribute to strengthening the literature.
Research limitations/implications
The FNN contributions are numerous and cannot be covered in a single study. The authors remain focused on learning algorithms and optimization techniques, along with their application to real-world problems, proposing to improve the generalization performance and convergence rate of FNNs with the characteristics of computing optimal hyperparameters, connection weights, hidden units, selecting an appropriate network architecture rather than trial and error approaches and avoiding overfitting.
Practical implications
This study will help researchers and practitioners to deeply understand the existing algorithms merits of FNNs with limitations, research gaps, application areas and changes in research studies in the last three decades. Moreover, the user, after having in-depth knowledge by understanding the applications of algorithms in the real world, may apply appropriate FNN algorithms to get optimal results in the shortest possible time, with less effort, for their specific application area problems.
Originality/value
The existing literature surveys are limited in scope due to comparative study of the algorithms, studying algorithms application areas and focusing on specific techniques. This implies that the existing surveys are focused on studying some specific algorithms or their applications (e.g. pruning algorithms, constructive algorithms, etc.). In this work, the authors propose a comprehensive review of different categories, along with their real-world applications, that may affect FNN generalization performance and convergence rate. This makes the classification scheme novel and significant.
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G. M. Han and X. Q. Deng
Color is the dominant element of visual communication design. For cities, color is one of their characteristics. Improving the overall style of a city through the control and…
Abstract
Color is the dominant element of visual communication design. For cities, color is one of their characteristics. Improving the overall style of a city through the control and planning of architectural color has been a hot issue in urban control and planning in recent years. Considering color pollution in urban planning, we analyze the important role of color in visual communication design from the perspective of the visual communication concept of media art. The current situation of residential building colors in Shanghai City is investigated. We determine the residents’ preferences and requirements related to architectural exterior color on the basis of a data analysis of color and material selection, color matching, composition, and others. This study changes the traditional architectural color design concept that centers on individuals and lacks overall control. Specifically, we construct a set of multi-scale color controlling and planning systems at the macro-, meso-, and micro-scales. We also guide and control the urban architectural color such that it is in accordance with the systematic color planning strategy. This method enables the systematic and holistic external color planning of urban residential buildings.
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Parniyan Khalili, Omid Asbaghi, Ladan Aghakhani, Cain C.T. Clark and Neda Haghighat
This study aims, a systematic review and meta-analysis, to evaluate the effects of folic acid (folate) on patients with depression.
Abstract
Purpose
This study aims, a systematic review and meta-analysis, to evaluate the effects of folic acid (folate) on patients with depression.
Design/methodology/approach
Related articles were found by searching PubMed, SCOPUS, Web of science and Cochrane’s Library, from inception to January 2022. Weighted mean differences (WMD) were pooled using a random-effects model. Heterogeneity, sensitivity analysis and publication bias were reported using standard methods.
Findings
Pooled analysis of six randomized controlled trials revealed that folic acid supplementation decreased the depression score in the Beck Depression Inventory (WMD: −3.9; 95% CI: −5.3 to −2.4, p < 0.001) compared with control group, without heterogeneity (I2 = 0.0%, p = 1.000). It also lowered the depression score in the Hamilton (HAM) Depression Inventory (WMD: −3.5 mg/dL; 95% CI: −4.6 to −2.4, p < 0.001) compared with control group, with moderate heterogeneity (I2 = 71.8%, p = 0.007). Moreover, subgroup analysis showed that the folic acid supplementation reduced HAM in all subgroups. Meta-regression analysis demonstrated that there is no evidence of a significant linear relationship between dose and duration of folic acid supplementation and changes in HAM. Also, based on the non-linear dose response, no evidence of a relationship between dose and duration of folic acid supplementation and changes in HAM was found.
Originality/value
Folic acid supplementation could possibly have an effect on lowering depression in patients. However, the clinical trials thus far are insufficient for clinical guidelines and practice.
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Rahmat Zaki Auliya, Muhamad Ramdzan Buyong, Burhanuddin Yeop Majlis, Mohd. Farhanulhakim Mohd. Razip Wee and Poh Choon Ooi
The purpose of this paper is to propose an alternative approach to improve the performance of microelectromechanical systems (MEMSs) silicon (Si) condenser microphones in terms of…
Abstract
Purpose
The purpose of this paper is to propose an alternative approach to improve the performance of microelectromechanical systems (MEMSs) silicon (Si) condenser microphones in terms of operating frequency and sensitivity through the introduction of a secondary material with a contrast of mechanical properties in the corrugated membrane.
Design/methodology/approach
Finite element method from COMSOL is used to analyze the MEMS microphones performance consisting of solid mechanic, electrostatic and thermoviscous acoustic interfaces. Hence, the simulated results could described the physical mechanism of the MEMS microphones, especially in the case of microphones with complex geometry. A 2-D model was used to simplify computation by applying axis symmetry condition.
Findings
The simulation results have suggested that the operating frequency range of the microphone could be extended to be operated beyond 20 kHz in the audible frequency range. The data showed that the frequency resonance of the microphone using a corrugated Si membrane with SiC as the embedded membrane is increased up to 70 kHz compared with 63 kHz for the plane Si membrane, whereas the microphone’s sensitivity is slightly decreased to −79 from −76 dB. Furthermore, the frequency resonance of a corrugated membrane microphone could be improved from 26 to 70 kHz by embedding the SiC material. Last, the sensitivity and frequency resonance value of the microphones could be modified by adjusting the height of the embedded material.
Originality/value
Based on these theoretical results, the proposed modification highlighted the advantages of simultaneous modifications of frequency and sensitivity that could extend the applications of sound and acoustic detections in the ultrasonic spectrum with an acceptable performance compared with the typical state-of-the-art Si condenser microphones.
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Mark Williams, Ying Zhou and Min Zou
This study aims to address the question of why organizations do not uniformly apply pay for performance (PFP) throughout the organization, focusing on the wider occupational…
Abstract
Purpose
This study aims to address the question of why organizations do not uniformly apply pay for performance (PFP) throughout the organization, focusing on the wider occupational structure in which they and the jobs they create are embedded. The authors propose a model of “occupational differentiation” whereby the probability of a job within a given organization having PFP increases with the levels of monitoring difficulty and requisite human asset specificity characterizing the occupation to which a job belongs, being highest in occupations characterized by high levels of both (generally managerial and professional occupations).
Design/methodology/approach
Using the Workplace Employment Relations Survey (a nationally representative matched employer–employee dataset for Britain), this paper investigates this question for all 350 occupations delineated by the UK's Office for National Statistics using regression methods that adjust for other confounding factors such as demographic factors and workplace fixed effects.
Findings
The authors find organizations “occupationally differentiate” the use of PFP in ways consistent with the model, i.e. PFP is most likely to be found in occupations characterized by both high monitoring difficulty and high requisite human asset specificity (mainly managerial and professional occupations) and least likely in occupations scoring low in both. The finding holds across PFP types (individual, group, organizational), whether organizations are large or small, and hold across most industrial sectors.
Research limitations/implications
The main implication of this study is that organizations appear to be taking into consideration whether the wider profession to which a job belongs when implementing PFP, irrespective of their own human resource management strategies and organizational context. There are a few limitations to this study, with the main one being that this model is mainly confined to empirical support is only found in the private sector. The public sector appears to be beyond the reach of the model, where PFP implementation is generally rarer. A second limitation is that the dataset is from 2011 and only covers a single country.
Practical implications
Given organizations appear to be implementing PFP based on occupation, this may lead to equity concerns, as different groups are being treated differently within organizations based upon their occupational group.
Social implications
As PFP jobs tend to pay more than non-PFP jobs and PFP prevalence has been growing, by being more likely to implement it for generally high-paid groups (generally higher managerial and professional occupations), PFP may contribute to wider pay differentials within and between organizations.
Originality/value
By introducing the occupational-level of analysis and the differential nature of tasks across occupational groups, the model offers a new midrange, sociological perspective to understanding intra-organizational dynamics in PFP use and potentially human resource practices more broadly.
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Waqar Ahmed Khan, S.H. Chung, Muhammad Usman Awan and Xin Wen
The purpose of this paper is three-fold: to review the categories explaining mainly optimization algorithms (techniques) in that needed to improve the generalization performance…
Abstract
Purpose
The purpose of this paper is three-fold: to review the categories explaining mainly optimization algorithms (techniques) in that needed to improve the generalization performance and learning speed of the Feedforward Neural Network (FNN); to discover the change in research trends by analyzing all six categories (i.e. gradient learning algorithms for network training, gradient free learning algorithms, optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) collectively; and recommend new research directions for researchers and facilitate users to understand algorithms real-world applications in solving complex management, engineering and health sciences problems.
Design/methodology/approach
The FNN has gained much attention from researchers to make a more informed decision in the last few decades. The literature survey is focused on the learning algorithms and the optimization techniques proposed in the last three decades. This paper (Part II) is an extension of Part I. For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications” is referred to as Part I. To make the study consistent with Part I, the approach and survey methodology in this paper are kept similar to those in Part I.
Findings
Combining the work performed in Part I, the authors studied a total of 80 articles through popular keywords searching. The FNN learning algorithms and optimization techniques identified in the selected literature are classified into six categories based on their problem identification, mathematical model, technical reasoning and proposed solution. Previously, in Part I, the two categories focusing on the learning algorithms (i.e. gradient learning algorithms for network training, gradient free learning algorithms) are reviewed with their real-world applications in management, engineering, and health sciences. Therefore, in the current paper, Part II, the remaining four categories, exploring optimization techniques (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) are studied in detail. The algorithm explanation is made enriched by discussing their technical merits, limitations, and applications in their respective categories. Finally, the authors recommend future new research directions which can contribute to strengthening the literature.
Research limitations/implications
The FNN contributions are rapidly increasing because of its ability to make reliably informed decisions. Like learning algorithms, reviewed in Part I, the focus is to enrich the comprehensive study by reviewing remaining categories focusing on the optimization techniques. However, future efforts may be needed to incorporate other algorithms into identified six categories or suggest new category to continuously monitor the shift in the research trends.
Practical implications
The authors studied the shift in research trend for three decades by collectively analyzing the learning algorithms and optimization techniques with their applications. This may help researchers to identify future research gaps to improve the generalization performance and learning speed, and user to understand the applications areas of the FNN. For instance, research contribution in FNN in the last three decades has changed from complex gradient-based algorithms to gradient free algorithms, trial and error hidden units fixed topology approach to cascade topology, hyperparameters initial guess to analytically calculation and converging algorithms at a global minimum rather than the local minimum.
Originality/value
The existing literature surveys include comparative study of the algorithms, identifying algorithms application areas and focusing on specific techniques in that it may not be able to identify algorithms categories, a shift in research trends over time, application area frequently analyzed, common research gaps and collective future directions. Part I and II attempts to overcome the existing literature surveys limitations by classifying articles into six categories covering a wide range of algorithm proposed to improve the FNN generalization performance and convergence rate. The classification of algorithms into six categories helps to analyze the shift in research trend which makes the classification scheme significant and innovative.
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Xin Ying, Zheng Liu, Guang Chen and Fengyuan Zou
The comfort and fit of clothes are affected by fabric properties, dressed ease and environmental conditions, in which dressed ease is influenced by the interaction among complex…
Abstract
Purpose
The comfort and fit of clothes are affected by fabric properties, dressed ease and environmental conditions, in which dressed ease is influenced by the interaction among complex shapes of human body, style design and fabric mechanical properties.
Design/methodology/approach
In this study, the dressed ease distribution at waist section, which is related to body surface convex angle, was investigated using 3D scanning. A series of surface convex angles on bust and back were formed after adjusting the mannequin. The mannequin was scanned by TC2 separately in garments with eight different ease allowances. Then the dressed ease distributions at waist under different convex angles of body surface have been acquired by calculating the distance between waist points and dressed surfaces along normal directions.
Findings
The results showed that the body surface convex angle was weakly related to the dressed ease when the garments’ bust ease allowance was below 4 cm. When the garments’ bust ease allowance was within 6–12 cm, the body convex angle had a great impact on the dressed waist ease distribution in the condition of 26º–33º bust convex angle and 13.96º–17.96º back slope angle. For slack garments with more than 16 cm ease allowance, the dressed waist ease distribution did not relate to the bust convex angle, while it strongly related to the bust convex angle between 13.96º and 17.96º. The regression model was statistically significant between the dressed ease value and the body surface convex angle.
Originality/value
According to the dressed waist ease distribution of different body surface convex angles, this paper gives an application of pattern modification in order to optimize the waist fit. The results can provide guidance for the optimization of different body shapes. At the same time, the application of gap data to 3D virtual fitting can greatly improve the authenticity of virtual simulation effect.
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Tong Sun and Wanyi Chen
Following the growing adoption of social media, many entrepreneurs are launching personal social media channels. This study focuses on the effect of entrepreneurs' shared…
Abstract
Purpose
Following the growing adoption of social media, many entrepreneurs are launching personal social media channels. This study focuses on the effect of entrepreneurs' shared information on We Media platforms on the value relevance of their earnings.
Design/methodology/approach
Using entrepreneurs' We Media data collected from A-share-listed companies on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2018, this study investigates the effect of the data on the value relevance of earnings using the modified Ohlson model. The authors applied textual analysis to retrieve entrepreneurial We Media data acquired manually from Weibo.
Findings
We Media platforms can increase the value relevance of earnings. Entrepreneurs can enhance investor trust by establishing social ties with investors. Investors are more likely to recognize earnings information publicized by enterprises, owing to internal consistency. Particularly, value relevance improves significantly with more personal information being posted and more “likes” being acquired on entrepreneurs' We Media accounts. This positive effect is more obvious in privately owned and highly marketized regions.
Originality/value
The findings extend the research on the economic consequences of We Media as an important information channel, enrich the research on the social media posting behavior of entrepreneurs and provide a reference for enterprises to instill trust using new information disclosure methods and for governments to establish a safe internet environment to promote the sustainable development of the capital market.