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The research aims to explore the influence mechanism of peer feedback and users' knowledge contribution behavior. This study draws on the social identity theory and considers social identity as a mediating factor into the research framework.

This paper collected users' activity data of 142,191 ideas submitted by 76,647 users from the MIUI community between October 2010 and May 2018 via Python software, and data were processed using Stata 16.0.

The results indicate that knowledge feedback and social feedback positively influence users' knowledge contribution (quantity and quality), respectively. User's cognitive identity positively mediates the relationship between peer feedback and knowledge contribution behavior, affective identity positively mediates the relationship between peer feedback and knowledge contribution behavior, while evaluative identity positively mediates the relationship between peer feedback and knowledge contribution quality, but there is no mediating effect between peer feedback and knowledge contribution quantity.

This study advances knowledge management by highlighting peer feedback on online innovation communities. By demonstrating the significant mediating effect of social identity, this study empirically clarifies the relationships of peer feedback (knowledge feedback and social feedback) to specific dimensions of knowledge contribution, thereby providing managerial guidance to the online innovation community on incentivizing and managing user interaction to foster the innovation development of firms.

Online brand communities (OBCs) are important platforms to obtain consumers' ideas. The purpose of this study is to examine how peer influence and consumer contribution behavior simulate innovative behaviors in OBCs to increase idea quality.

Using a firm-hosted popular online brand community – Xiaomi Community (MIUI), the authors collected a set of data from 6567 consumers and then used structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to empirically test the impact of peer influence and consumer contribution behaviors on idea quality in OBCs.

The results of this study show that both peer influence breadth and depth have a positive effect on idea adoption and peer recognition, wherein proactive contribution behavior positively mediates these relationships, and responsive contribution behavior negatively mediates the impact of peer influence breadth and peer influence depth on peer recognition. A more detailed analysis using the fsQCA method further identifies four types of antecedent configurations for better idea quality.

Based on the attention-based view and the theory of learning by feedback, this study explores the factors that affect idea quality in the context of social networks and extends the research of peer influence in the digital age. The paper helps improve our understanding of how to promote customer idea quality in OBCs.

The purpose of this paper is to present a detection method based on computer vision for automatic flexible printed circuit (FPC) defect detection.

This paper proposes a new method of watershed segmentation based on morphology. A dimensional increment matrix calculation method and an image segmentation method combined with a fuzzy clustering algorithm are provided. The visibility of the segmented image and the segmentation accuracy of a defective image are guaranteed.

Compared with the traditional one, the segmentation result obtained in this study is superior in aspects of noise control and defect segmentation. It completely proves that the segmentation method proposed in this study is better matches the requirements of FPC defect extraction and can more effectively provide the segmentation result. Compared with traditional human operators, this system ensures greater accuracy and more objective detection results.

The extraction of FPC defect characteristics contains some obvious characteristics as well as many implied characteristics. These characteristics can be extracted through specific space conversion and arithmetical operation. Therefore, more images are required for analysis and foresight to establish a more widely used FPC defect detection sorting algorithm.

This paper proposes a new method of watershed segmentation based on morphology. It combines a traditional edge detection algorithm and mathematical morphology. The FPC surface defect detection system can meet the requirements of online detection through constant design and improvement. Therefore, human operators will be replaced by machine vision, which can preferably reduce the production costs and improve the efficiency of FPC production.

This paper aims to propose a novel method to predict and alleviate feature fatigue. Many products now are loaded with an extensive number of features. Adding more features to one product generally makes the product more attractive on the one hand but, on the other hand, may result in increasing difficulty to use the product. This phenomenon is called “feature fatigue”, which will lead to dissatisfaction and negative word-of-mouth (WOM). Feature fatigue will damage the brand’s long-term profit, and ultimately decrease the manufacturer’s customer equity. Thus, a problem of balancing the benefit of increasing “attractiveness” with the cost of decreasing “usability” exists.

A novel method based on the Bass model is proposed to predict and alleviate feature fatigue. Product capability, usability and WOM effects are integrated into the Bass model to predict the impacts of adding features on customer equity in product development, thus helping designers alleviate feature fatigue. A case study of mobile phone development based on survey data is presented to illustrate and validate the proposed method.

The results of the case study demonstrate that adding more features indeed increases initial sales; however, adding too many features ultimately decreases customer equity due to usability problems. There is an optimal feature combination a product should include to balance product capability with usability. The proposed method makes a trade-off between initial sales and long-term profits to maximize customer equity.

The proposed method can help designers predict the impacts of adding features on customer equity in the early stages of product development. It can provide decision supports for designers to decide what features should be added to maximize customer equity, thus alleviating feature fatigue.

Feature fatigue (FF) will lead to negative Word-Of-Mouth (WOM), which damages the brand’s long-term profit and ultimately decreases the manufacturer’s customer equity (CE). It becomes severer in multi-generation products because of the significant impacts of earlier generation products on the CE of later ones. The purpose of this paper is to alleviate FF, it is imperative for designers to decide what features should be integrated to balance initial revenue and long-term profit so as to maximize CE.

In this paper, a novel method based on the Norton-Bass model is proposed to alleviate FF of multi-generation products to help designers find optimal feature combination that maximizes CE. The authors take the effects of adding features on product capability and usability into account, and integrate product capability, usability, WOM and earlier-generation product’s effects into the Norton-Bass model to predict the impacts of FF on CE in current product development. A case study of a virtual product is presented to illustrate and validate the proposed method.

The advantage of the proposed method is highlighted in the cases of large feature number, high-product complexity (low-product usability) and multi-generation products. The experiments show that the earlier generations do affect the later ones from the perspective of maximizing CE. The superiority of the proposed method compared with the traditional way to put all potential features into a product during the product development is demonstrated. And the more features, the larger CE obtained using the proposed model than the one obtained by traditional way.

Although, there are reports attempting to analyze and alleviate FF, most of these studies still suffer the limitations that cannot point out what features should be added to the product with the objective of maximizing CE. In addition, few studies have been carried out to alleviate FF of multi-generation products. A novel method based on the Norton-Bass model and a genetic algorithm is proposed to alleviate FF of multi-generation products to help designers find optimal feature combination that maximizes CE.

Estimating product cost at the design stage, often referred to as product costing, is vitally important in the product configuration process in the mass customization paradigm. There are three research streams in this field: analogous, statistical, and analytical methods. However, in a mass customization environment, these methods suffer the drawbacks of poor accuracy, low‐agility and an undesirable degree of detail. It is imperative to develop an efficient framework for product costing in mass customization. The paper aims to address these issues.

Activity‐based costing (ABC) becomes a new trend in product costing which alleviates the shortcomings discussed above. However, the complexity of ABC probably increases in mass customization with a high degree of variety. In this research, a framework is formalized for product costing in mass customization, in which a generic activity definition is proposed to simplify and catalyze ABC practice in high‐variety production. Moreover, an activity dictionary with a hierarchical structure is developed to bring commonality and modularity to the storage and retrieval of activity data.

A generic activity‐dictionary‐based method for product costing in mass customization is developed.

The method proposed for product costing provides valuable cost data for product specifications, as well as the activities to create them. Therefore, a product configuration solution can be financially evaluated and optimized.

Based on the methodology proposed, a prototype system has been developed and implemented in a computer‐assembling company.

This paper alleviates the negative impact of activity variety on product costing.

Making decisions on production and maintenance separately, as is often done in practice and research literature, may not result in overall optimization. This paper aims to propose a joint method that better integrates production planning and maintenance at the tactical level. The potential of improving the performance of the classic planning method is also explored.

An integrated production planning and maintenance model is proposed. The production capacity losses resulted from both preventive and corrective maintenance activities are considered. Meanwhile, the reliability deterioration of the machine is considered to be operation dependent. An iterative approach is presented to find a solution for the nonlinear model through iteratively solving a sequence of mixed integer linear programming instances, accompanied by modification of some parameters prior to each iteration. Computational experiments are conducted to evaluate the performance of the proposed method compared with three other methods, including two methods based on separate planning and one integrated model.

The superiority of the proposed method compared with all the other three methods is demonstrated. Thus, the values of both integrated planning and considering operation-dependent failures are testified. The advantage of the proposed method is highlighted in the cases of high capacity utilization, long maintenance durations and low maintenance costs. The performance of the two methods based on separate planning is sensitive to the system utilization, and when utilization is high, the one with an availability-sensitive objective function defined for the maintenance problem performs better.

Few studies have been carried out to integrate decisions on production lot and maintenance. Their considerations are either incomplete or not realistic enough. A more comprehensive and realistic integrated model is proposed in this paper, along with an iterative solution algorithm for it. A potential way to improve the performance of the classic planning method with its simplicity preserved is also presented.

Fifth-generation (5G) networks play a significant role in handover methods. 5G wireless network is open, flexible and highly heterogeneous along with the overlay coverage and small cell deployments. Handover management is one of the main problems in the heterogeneous network. Also, handover satisfies the needs of ultra-reliable communications along with very high reliability and availability in 5G networks. Handover management deals with every active connection of a user’s device, which moves the connection between the user’s device and the counterparty from one network point to another. Thus, the handover decision determines the best access network and also decides whether the handover is performed or not.

The main intention of this survey is to review several existing handover technologies in 5G. Using the categories of analysis, the existing techniques are divided into different techniques such as authentication-based techniques, blockchain-based techniques, software-defined-based techniques and radio access-based techniques. The survey is made by considering the methods such as used software, categorization of methods and used in the research works. Furthermore, the handover rate is considered for performance evaluation for the handover techniques in 5G. The drawbacks present in the existing review papers are elaborated in research gaps and issues division.

Through the detailed analysis and discussion, it can be summarized that the widely concerned evaluation metric for the performance evaluation is the handover rate. It is exploited that the handover rate within the range of 91%–99% is achieved by three research papers.

A survey on the various handover mechanisms in 5G networks is expected in this study. The research papers used in this survey are gathered from different sources such as Google Scholar and IEEE. Also, this survey suggests a further extension for the handover mechanism in 5G networks by considering various research gaps and issues.

Building on social media and destination brand-related literature, this study aims to explore World Heritage Sites’ (WHSs) brand diffusion and formation process from long-term and short-term perspectives, which includes brand diffusion, user-generated content (UGC), opinion leaders and brand events’ impact.

This study uses a mixed-method including text mining, keyword analysis and social network analysis to explore the brand formation process of four popular WHSs in Beijing, namely, the Palace Museum, Great Wall, Summer Palace and Temple of Heaven and more than 10,000,000 users’ data on Sina Weibo has been implemented to uncover the underlying social media branding mechanism.

The results show that the number of postings keeps in a stable range in most months, but, in general, there are no common rules for changing trends among the four WHSs; long-term high-frequency keywords related to history and culture account for a higher percentage; different kinds of accounts have varying impacts on information diffusion, in which media accounts lead to a bigger influence. However, more followers do not necessarily mean more interactions and most of the interaction ratio is much lower than 0.01000; brand events facilitate brand dissemination and have an impact on the creation of UGC.

This study is valuable for destination marketers to deeper understand brand diffusion and formation and provides valuable insights for developing effective destination marketing strategies.

Unlike previous studies that only concern a few parts of destination brand formation via social media (e.g. brand diffusion, brand events or opinion leaders’ impact), this study takes a more comprehensive perspective by systematically analyzing the brand formation process of WHSs on social media. By considering both long-term diffusion and short-term representative events, this study provides a more holistic understanding of the branding mechanism.

The use of brand slogans that represent brand concepts on app launch pages can improve user brand impressions. The purpose of this paper is to investigate the impact of using animated or static spokes-characters with brand slogans on app launch pages.

Using the theory of attention selection, the authors conducted two experiments to study the boundary and mediation path of the influence of the motion attributes of spokes-characters (static vs animated) on brand memory based on app launch time (3 s vs 5 s), user engagement with spokes-characters and the level of attention to brand slogans.

Study 1 explores the effect of the interaction between launch time and the motion attributes of spokes-characters on brand memory. The results show that when the launch time of the app is 3 s, the advertisement memory effect of using a static spokes-character is better than that of using an animated spokes-character; when the launch time of the app is 5 s, the advertisement memory effect of using an animated spokes-character is better than that of using a static spokes-character. Study 2 shows that user engagement with spokes-characters and the level of attention given to brand slogans play a continuous mediating role in the effect of the interaction between launch time and the motion attributes of spokes-characters on brand memory.

This paper contributes to the marketing literature by expanding the knowledge of spokes-characters and animated visual images, providing new insights for future research.