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The purpose of this paper is to evaluate the changes in descriptive sensory properties and overall consumer acceptability of soymilk prepared from roasted soybeans.

In total, 12 purposively selected post graduate students majoring in Food Science conducted descriptive sensory analysis after being trained for 18 h in sensory analysis, while 75 untrained students conducted consumer acceptability test of soymilk prepared by roasting soybeans at a temperature of 110°C for 20, 40, 60, 80 and 100 min and at 120°C for 20 min.

Results have revealed that roasting soybeans improved sensory properties by significantly (p<0.05) decreasing the objectionable green, beany flavours and increasing sweet taste, viscosity and roasted flavour. Furthermore, results from the principal component analysis revealed that aroma and sweet taste were the most critical sensory attributes. In addition, it was found out that soymilk samples prepared by roasting soybeans at 110°C for 40 and 60 min and at 120°C for 20 min were significantly more acceptable than the control soymilk.

The participants in this study were from one locality and predominantly soybean consuming community and therefore there is need to conduct the study in a different locality in order to validate the study findings.

The study can assist small scale processors that might not have access to lipoxygenase-free soybeans and other technologies for improving the quality of soymilk.

The study can be used as a guide for connecting the food processers with the external world of consumption.

For the first time, the study findings have demonstrated that controlled soybean roasting can be a useful strategy for improving soymilk sensory properties and consumer acceptability. The findings in this study can be usefully used in the quality control of soy bean-based products.

In the era of mobile internet, the social Q&A community has built a large-scale and complex knowledge label network through its internal knowledge units, and the scale and structure of the network have changed over time. By analysing the structural characteristics and evolution rules of knowledge label networks, the main purpose of this study is to understand the internal mechanisms of the replacement of old and new knowledge and the expansion of knowledge element boundaries, so as to explore the realization path of knowledge management in the new era from the perspective of complex networks.

This paper uses distributed crawlers to capture 419,349 samples from the Zhihu platform. Each sample contains 33 characteristic dimensions, and the natural year is used as the sliding window to divide the whole. In this study, the global knowledge label network and 11 local knowledge label networks are first constructed. Then, the degree distribution analysis and central node exploration of the knowledge label network are carried out using the complex network method. Finally, the average shortest path and average clustering coefficient of the network are analysed by the time series method, and the ARIMA model is used to predict the evolution of the correlation coefficient.

The research results show that the dissimilation degree of the degree distribution of the knowledge label network has gradually decreased from 2011 to 2021, and the attention of users in the knowledge community has shown a trend of distraction and diversification over time. With the expansion of the scale of the knowledge label network and the transformation to an information network, the network sparsity is becoming more and more obvious, and the knowledge granularity of the Q&A community is being refined and diversified. The prediction of the correlation coefficient of the knowledge label network by the ARIMA model shows that the connection between the labels is lacking diversity and the opinion strengthening phenomenon tends to strengthen, which is more likely to form the “echo chamber effect”, resulting in mutual isolation and even opposition between different circles. The Q&A community is about to enter a mature stage, and the corresponding status of each label has been finalized. The future development trend of label networks will be reflected in the substitution between labels, and the specific structure will not change significantly.

The Q&A community model is the trend in Web 2.0 community development. This study proves the effectiveness of complex networks and time series prediction methods in knowledge label network mining in the Q&A community.

Electronic waste (e-waste) such as discarded computers and smartphones may contain large amounts of confidential data. Improper handling of remaining information in e-waste can, therefore, drive information security risk. This risk, however, is not always properly assessed and managed. The authors take the protection motivation theory (PMT) lens of analysis to understand intentions to protect one's discarded electronic assets.

By applying structural equation modeling, the authors empirically tested the proposed model with survey data from 348 e-waste handling users.

Results highlight that (1) protection intention is influenced by the perceived threat of discarding untreated e-waste (a threat appraisal) and self-efficacy to treat the discarded e-waste (a coping appraisal) and (2) optimism bias plays a dual-role in a direct and moderating way to reduce the perceived threat of untreated e-waste and its effect on protection intentions.

Results support the assertions and portray a unique theoretical account of the processes that underline people's motivation to protect their data when discarding e-waste. As such, this study explains a relatively understudied information security risk behavior in the e-waste context, points to the role of optimism bias in such decisions and highlights potential interventions that can help to alleviate this information security risk behavior.

Free trial is an effective strategy to gaining users’ data so as to strengthen and optimize product design. The purpose of this paper is to understand the IT companies' dynamic decision-making behavior in the free trial of IT products and services context based on a three-stage theoretical framework and users' decision-making behavior in the respective stage.

A three-stage methodology is proposed to clarify relevant decision problems and actions in each stage from IT companies' and users' perspectives, respectively. It then investigates relating variables on IT companies' decision-making based on extant research and users' decision-making.

In this study, the authors argue that the IT companies have to make the offering, implementation and retention decision in different stage during the whole free trial process. Each decision is determined by several variables from their own and users, namely the offering decision is determined by product characteristics, network effects, product life cycle and WOM (word of mouth); the implementation decision is determined by the quality of products and services, trial type, incentive measures on user's usage and communication strategy; and the retention decision is determined by the product and price strategy.

The results are practical and can be used by IT companies as a decision basis or reference to make reliable decisions so that IT companies can take target measures to ensure the effectiveness of their free trial strategy so as to meet their users' needs based on products designed by data driven. Thus, the ultimate goal of supply chain management is achieved.

In this study, the decision-making process in the free trial of IT products and services context is investigated as a whole for the first time. From the IT companies' perspective, the process includes offering, implementation and retention decision stages, which are continuous and inseparable. The variables that determine IT companies' decision-making are identified based on users' decision and action. Hence, it represents a brand-new whole process perception to clearly understand the dynamic of the IT companies' decision-making. Considering users' decision and action, the final decisions of the IT companies will be more practical in respect of motivating, retaining and upgrading users.

Online dating services have been growing rapidly in recent years. However, adopting these services may involve high risk and trust issues among potential users toward both online dating services and the daters they introduce to users. The purpose of this paper is to investigate how perceived benefits vs risks, and trust vs distrust affect user adoption vs non-adoption intentions toward using this rather controversial information and communications technology in the context of online dating.

Structural equation modeling was used to evaluate the research model using data from a survey of 451 single individuals.

The results indicated that perceived benefits play more essential roles in adoption, while perceived risks affect non-adoption more. Individuals' trust in online dating service predicts a major portion of the variation in user benefit perceptions, while distrust in online dating service and in daters that users might select significantly influence perceived risks. Moreover, benefit and risk perceptions can mediate the impacts of trust and distrust on both adoption and non-adoption decisions.

This study extends theories of decision-making in the use of controversial information technologies such as in the case of online dating. It investigates the coexistence of various trust and distrust beliefs as well as benefit and risk perceptions, and their different impacts on adoption and non-adoption in online dating services.

The purpose of this paper is to reveal the pattern between government innovation funding and enterprise value creation. Many factors, including government innovation funding, R&D ability, corporate governance and some company characteristics significantly affected the efficiency of firm value creation.

This paper proposed a novel methodology based on clustering-rough sets to explore the characteristics of enterprise value creation behavior, and map the relationship between government innovation funding and enterprise value creation. The agglomerative hierarchical clustering (AHC) algorithm were used to classify firm performance and get two types of value creation efficiencies and to discretize condition attributes because the rough set theory cannot deal with continuous attributes. This paper utilized the rough sets method to realize data mining and get rules of government innovation funding and enterprise value creation.

R&D ability, proportion of independent directors, remuneration of directors, operating revenue, number of employees, price-earnings ratio, quick ratio, capital intensity and ROA were important to identify firm value creation efficiency when government funded the firms. Firms of high level of government innovation funding, high lagged R&D ratio, high remuneration of directors, low price-earnings ratio, low quick ratio, moderate capital intensity and high ROA were more likely to have high efficiency of value creation.

Since China implemented the innovation-driven development strategy, facilitating enterprise innovation has become an important way to achieve high-quality economic growth. With constantly increasing of Chinese government innovation funding, studying on the effect of government innovation funding on firm’s value creation is significant to improve the efficiency of government resource allocation. It is valuable to reveal the pattern between government innovation funding and enterprise value creation based on the value added theory. The rules obtained could be used to provide decision-making support to improve the efficiency of government innovation funding and prevent waste of government resources effectively.