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Because of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the common needs of all consumers for a kind of experiential product with short life cycle (EPSLC). Methods for predicting TSV of long-life-cycle products may not be suitable for EPSLC. Furthermore, point prediction cannot obtain satisfactory prediction results because information available before production is inadequate. Thus, this paper aims at proposing and verifying a novel interval prediction method (IPM).

Because interval prediction may satisfy requirements of preproduction investment decision-making, interval prediction was adopted, and then the prediction difficult was converted into a classification problem. The classification was designed by comparing similarities in attribute relationship patterns between a new EPSLC and existing product groups. The product introduction may be written or obtained before production and thus was designed as primary source information. IPM was verified by using data of crime movies released in China from 2013 to 2017.

The IPM is valid, which uses product introduction as input, classifies existing products into three groups with different TSV intervals, mines attribute relationship patterns using content and association analyses and compares similarities in attribute relationship patterns – to predict TSV interval of a new EPSLC before production.

Different from other studies, the IPM uses product introduction to mine attribute relationship patterns and compares similarities in attribute relationship patterns to predict the interval values. It has a strong applicability in data content and structure and may realize rolling prediction.

This paper investigates the semantic association mechanisms of weak demand signals that facilitate innovative product development in terms of conceptual and temporal precedence, despite their inherent ambiguity and uncertainty.

To address this challenge, a domain ontology approach is proposed to construct a customer demand scenario-based framework that eliminates the blind spots in weak demand signal identification. The framework provides a basis for identifying such signals and introduces evaluation indices, such as depth, novelty and association, which are integrated to propose a three-dimensional weak signal recognition model based on domain ontology that outperforms existing research.

Empirical analysis is carried out based on customer comments of new energy vehicles on car platform such as “Auto Home” and “Bitauto”. Results demonstrate that in terms of recognition quantity, the three-dimensional weak demand signal recognition model, based on domain ontology, can accurately identify six demand weak signals. Conversely, the keyword analysis method exhibits a recognition quantity of four weak signals; in terms of recognition quality, the three-dimensional weak demand signal recognition model based on domain ontology can exclude non-demand signals such as “charging technology”, while keyword analysis methods cannot. Overall, the model proposed in this paper has higher sensitivity.

This paper proposes a novel method for identifying weak demand signals that considers the frequency of the signal's novelty, depth and relevance to the target demand. To verify its effectiveness, customer review data for new energy vehicles is used. The results provide a theoretical reference for formulating government policies and identifying weak demand signals for businesses.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The purpose of this paper is to explore how the number and quality of games that publishers have released, popularity of game genre, age rating and package size are configured to determine the mobile game takeoff in a short time.

Based on the signaling theory, the authors present a conceptual model. Using actual data about 170 mobile games at their initial stage in the Apple App store, the authors test the conceptual model by applying fuzzy qualitative comparative analysis (fsQCA).

The findings identify four solutions that explain Mobile game takeoff in a short time. The authors highlight the role of the number and quality of games released by publishers, as well as that of popular game genres, which are always core factors when present.

This paper complements the previous research on the diffusion of mobile games by exploring which information combinations can lead to mobile games takeoff in a short time from the perspective of configuration. FsQCA serves as a better tool for explaining the complex relationships among variables than a regression analysis approach does. The authors extend existing knowledge on how the number and quality of games that publishers have released, popularity of game genre, age rating and package size combine to lead to takeoff of mobile games in a short time.

The study aims to show how several factors interact to promote mobile game download: the number of games released by a publisher, the quality of the games released, the popularity of a game's genre, the quality of borrowed intellectual property, the frequency of recommendations, intragenre ranking, consumer rating and review quantity.

Signaling theory was used to classify the mobile game information displayed on the Apple App Store into four groups. A conceptual model was proposed to illustrate the complex relationship between the information and download. Based on information on 203 mobile games in the seven days following their release, the model was empirically tested to identify the influence of information configuration on game download by combining fuzzy qualitative comparative analysis (fsQCA) and a fuzzy cognitive map (FCM).

Three solutions were identified for high game download and two for low/medium. The number of previous games released by a publisher, intragenre ranking, consumer rating and review quantity are core conditions that reinforce high game download. The effects of one information type on another and on downloads change as coexisting information types change.

This study enriches existing knowledge about how combinations of multiple types of game information lead to game download and extends previous variance-based research. Combining an FCM with fsQCA can facilitate one’s understanding of the complex causal relationships between game information and download.

This paper seeks to develop a conceptual model that enables understanding of consumer purchase decisions in relation to the first customized products, including steps of the decision process, determinants, and consequences of each step.

The classic grounded theory approach was used.

The study found that the process consists of six steps called knowledge learning and preliminary information searching, problem recognition, budget setting, detailed information searching and solution creating, purchase involvement, and use and post‐purchase. The process is distinct from the process of traditional models of consumer decision mainly from three aspects: the process starting from “knowledge learning and information searching”, not from “problem recognition”, while “problem recognition” is the second step; information searched in the process mainly on components of products, with little on products; and the process includes a step “solution creating”, but without steps “alternative evaluation” and “choice”. The consequences of the first step consist of knowledge mastered, market information acquired, pre‐decisions, perceived value of customized PC, positive attitude towards customized PC, and self‐confidence, which are the main determinants of the following steps. Besides these determinants, others included are interest, curiosity, work with computers as tools, sources of knowledge and information on PC, and income.

The study provides a foundation for future quantitative research on determinants of consumers' purchasing the first customized products and provides insights into marketing: for example, target consumers of customized PC are characterized at least by having great self‐confidence in their capability to make an effective decision in purchasing customized PC, and having mastered enough PC knowledge.

Across a variety of domains, consumers are demanding increased customized products. While consumer decision making has become a prominent research topic in various fields of marketing and consumer science over the past decades, surprisingly little research has examined consumer purchase decisions in relation to the first customized personal computer (PC) and factors influencing such a process. The study may fill this gap.

This paper seeks to empirically identify factors influencing Chinese consumers' intention to purchase customized desk top (PC for short) and their effect levels.

Survey and structural equation modeling techniques were used.

This research finds that: attitude toward purchasing customized PC, followed by self‐confidence, and subjective norm influence behavioral intention most significantly; perceived knowledge has a very strong and positive effect on self‐confidence and attitude; and subjective norm and perceived usefulness influence attitude positively. In contrast, a direct effect of perceived knowledge on behavioral intention is rejected. Experience for males and females moderates the confirmed relationships except the relationship between perceived knowledge and attitude for females. Gender for respondents with and without experience moderates the confirmed relationships except the effects of attitude and subjective norm on behavioral intention for respondents without experience and the effect of self‐confidence on behavioral intention for respondents with experience.

It appears that customized PC providers should be aware that mass customization is applicable to markets where consumers are familiar with PC, hold a positive attitude toward purchasing a customized PC, and have confidence in their capability to make an effective decision in purchasing a customized PC.

Little attention has been paid to empirical testing factors and their effect levels on consumers' intention to purchase customized products. No research has been conducted to empirically identify factors influencing Chinese consumers' intention to purchase customized PC and their effect levels, while this research fills this gap.

Share pledge is a popular way to raise funds in China, but it aggravates information asymmetry. As an indispensable information intermediary in the financial market, media coverage affects asset price and pricing efficiency and impacts information asymmetry. This study aims to explore the governance role of media coverage as an information intermediary in the share pledge context in China.

Moderating effect and mediating effect analyses are the primary methods used to test the governance role of media coverage. The ordinary least squares model was used to test the relationship between share pledge and market performance and then proved the moderating effect of media coverage toward the corporate market value of pledge firms. Accounting earnings value relevance models were explored to test the path of media coverage on firm market value by mediating effect analysis. At last, subgroup tests were used to verify the heterogeneity of the moderating effect of media coverage.

In the context of share pledge in China, the higher the share pledge ratio, the higher is the market value of listed firms, which verifies the motivation of controlling shareholders to avoid the transfer of control right and the motivation to tunneling. Media coverage has a significant negative moderating effect on the relationship between share pledge rate and corporate value and has a significant impact on the accounting earnings value relevance of share pledge firms. From the perspective of long-term earnings, media coverage reduces the market performance of share pledge firms by reducing the value correlation of accounting earnings information. From the short-term price point of view, media coverage reduces the market performance of share pledge firms by improving the value correlation of accounting earnings information. Furthermore, media coverage has a more significant moderating effect in state-owned share pledge firms and low information transparency and low information disclosure quality firms.

This paper does not distinguish the mode difference of spreading news and the impact of non-pledge media coverage. Also, this paper does not consider factors other than accounting information value relevance when exploring how media coverage affects the corporate market value. Share pledge firms should use media for publicity and play a role in media governance and should actively improve their information disclosure quality, strengthen communication with investors and reduce information asymmetry fundamentally.

This paper diversify the governance choices for share pledge firms and has important implications for firms, investors, information intermediaries and regulators. Media reports play an increasingly important role today, and any reports and predictions of major events may profoundly affect investors’ decisions. Although media reports can make up for the weakness of accounting information disclosure of equity pledge companies in some sense, it is still not a long-term strategy. Equity pledge companies should not only make use of media for publicity and play a role of media governance but also actively improve their information disclosure quality.

This paper focuses on share pledge firms to carry out in-depth research. Based on exploring the influence mechanism of share pledges, the authors find the importance of media governance. This paper expands the literature about the economic consequences of share pledges and provides empirical data for media governance of share pledge firms. This paper innovatively proves the governance role of media coverage from the view of accounting information value relevance. The main innovation point is the long and short-term perspective analysis of the influence of media coverage on the correlation of accounting earnings value. The heterogeneity effect analysis of media coverage also reflects the depth and strong practical guiding significance of this study.

This paper aims to review the latest applications in terms of three-dimensional printed (3DP) metal implants in orthopedics, and, importantly, the design of 3DP metal implants through a series of cases operated at The Second Hospital of Jilin University were presented.

This paper is available to practitioners who are use 3DP implants in orthopedics. This review began with the deficiency of traditional prostheses and basic concepts of 3DP implants. Then, representative 3DP clinical cases were summarized and compared, and the experiences using customized prostheses and directions for future potential development are also shown.

The results obtained from the follow-up of clinical applications of 3DP implants show that the 3D designed and printed metal implants could exhibit good bone defect matching, quick and safe joint functional rehabilitation as well as saving time in surgery, which achieved high patient satisfaction collectively.

Single center experiences of 3DP metal implants design were shared and the detailed technical points between various regions were compared and analyzed. In conclusion, the 3DP technology is infusive and will present huge potential to reform future orthopedic practice.

This study investigated the travel intentions (TIs) of Chinese travelers and their utilization of virtual tourism technology during the Zero COVID-19 policy period by using a stimulus-organism-response (SOR) model. The study specifically examines the interplay between knowledge of policy, perception of risk, TI, usage of virtual tourism technology (UVTT) and the mediating role of personal values.

The data were collected from 333 Chinese travelers through an online questionnaire, and structural equation modeling (SEM) was employed to test the proposed hypotheses.

The study suggests that knowledge of policy and risk perception increase changes in personal values (PVs), which, in turn, affect Chinese travelers' TIs and the UVTT, with PVs playing a mediating role. Risk perception has a positive effect on the UVTT.

This study highlights the positive impact of tourism policy knowledge and risk awareness on individual values as a stimulus. Stakeholders need to implement industry-specific policies that are in line with scientific developments. Tourism managers should prioritize understanding the psychological reactions of tourists in crises and provide support to mitigate negative emotions. Anticipating changes in PVs is crucial, as instability affects tourists' behavior. The findings of the study also provide valuable insights for technology designers and underscore the substitutability of virtual technologies in improving the tourism experience.

This study is the first to examine the mediating role of PVs in the relationship between knowledge of policy, tourism risk perception (TRP), TI and the UVTT based on the SOR model. The insights gained from this analysis can assist policymakers and tourism managers in understanding the psychological changes of tourists, thereby facilitating the development of appropriate tourism planning.