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The purpose of this paper is to examine the effect of peer pressure on joint consumption decisions among emerging adults. Building on prospect theory and characteristics of emerging adulthood, the authors propose that influence from peers (i.e. informational and normative influence) serves as a channel to understand how peer pressure shapes joint consumer behaviors at different levels of social capital.

An online survey is distributed to the emerging adults, aged 18 to 25, in the south, west, east and middle of the USA. Construct validity and reliability are tested by using confirmatory factor analysis. Structural equation modeling is used to test the mediating and moderating effects.

The results show that social capital moderates the relationship between peer pressure and group-oriented consumer decisions, such that the relationship is positive in groups with high-level social capital but negative in groups with low-level social capital. Furthermore, such effects tend to be achieved via peer influence. And peer influence is stronger in groups with high-level social capital than those with low-level social capital.

The current literature has shown contradictory results: it is usually believed that emerging adults may conform to pressure and engage in group-oriented decisions; however, some research has reported the opposite result. To better understand this relationship, the authors aim at a group-level factor – perceived social capital – as a boundary condition. This research contributes to the young consumer decision-making literature by involving the interplay among peer pressure, perceived social capital and peer informational and normative influence.

This paper aims to investigate the dynamics of a coordinate monopoly with established, risk-neutral firms; how these firms compete by adjusting prices; and when new competition(s) might enter the market with expectations of making more profits than any of the incumbents.

All results are established on the intuition of systems research and the concept of Nash equilibria of game theory.

Established results include how in a developed marketplace, risk neutrality would lead to stagnation in profits and irrational decision on pricing, and a sufficient and necessary condition under which new competition(s) would enter, although the market is coordinately monopolized.

Conclusions presented are initial results in the direction of research. More real-life-like, asymmetrical conditions need to be considered to see what variations of similar results could hold true.

Presented are an operational aspect on how to compete although the consequent level of profits might not change much or any at all and how some of the key issues on market entry timing are theoretically resolved.

This work indicates that for the minimum objective of business survival, existing firms, no matter how established, have to without any choice participate in market competition and look for new market opportunities.

Other than the established thought-provoking results, this work is the first of its kind. It organically combines recent achievements of systems research with game theory to explore issues of marketing, market entry timing and the dynamics of business firms’ behaviors.

This paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.

Because of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.

The results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.

The novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.

The proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

Ranking and comparing grey numbers represent a very important decision-making procedure in any given grey environment. The purpose of this paper is to study the existing approaches of ordering interval grey numbers in the context of decision making by surveying existing definitions.

Different methods developed for comparing grey numbers are presented along with their disadvantages and advantages in terms of comparison outcomes. Practical examples are employed to show the importance and necessity of using appropriate methods to compare grey numbers.

Most the available methods are not suitable for pointing out which number is larger when the nuclei of the grey numbers of concern are the same. Also, these available methods are also considered in terms of partial order and total order. Kernel and degree of greyness of grey numbers method is more advantageous than other methods and almost eliminates the shortcomings of other methods.

Different methods for ranking grey numbers are presented where each of them has disadvantages and advantages. By using different methods, grey interval numbers are compared and the results show that some methods cannot make grey number comparisons in some situations. The authors intend to find a method that can compare grey numbers in any situation. The findings of this research can prevent errors that may occur based on inaccurate comparisons of grey numbers in decision making. There are various research studies on the comparison of grey numbers, but there is no research on the comparison of these methods and their disadvantages, advantages or their total or partial order.

The purpose of this paper is to present a mixed integer programming model for simple assembly line balancing problems (SALBP) with Type 1 when the annual demand and task durations are uncertain and encoded with grey numbers.

Grey theory and grey numbers are used for illustrating the uncertainty of parameters in an SALBP, where the objective is to minimize the total number of workstations. The paper proposes a 0-1 mathematical model for SALBP of Type 1 with grey demand and grey task durations.

The uncertainty of the demand and task durations are encoded with grey numbers and a well-known 0-1 mathematical model for SALBP of Type 1 is modified to find the minimum number of workstations in order to meet both the lower and upper bounds of the uncertain demand. The results obtained from the proposed mathematical model show a task-workstation assignment that does not distribute precedence relations among tasks and workstations and the sum of task durations in each single workstation is less than or equal to the grey cycle time.

The grey theory and grey numbers have not been previously used to identify uncertainties in assembly line balancing problems. Therefore, this study provides an important contribution to the literature.

The purpose of this paper is to analyze the mechanism and filter efficacy of accumulation generation operator (AGO)/inverse accumulation generation operator (IAGO) in the frequency domain.

The AGO/IAGO in time domain will be transferred to the frequency domain by the Fourier transform. Based on the consistency of the mathematical expressions of the AGO/IAGO in the gray system and the digital filter in digital signal processing, the equivalent filter model of the AGO/IAGO is established. The unique methods in digital signal processing systems “spectrum analysis” of AGO/IAGO are carried out in the frequency domain.

Through the theoretical study and practical example, benefit of spectrum analysis is explained, and the mechanism and filter efficacy of AGO/IAGO are quantitatively analyzed. The study indicated that the AGO is particularly suitable to act on the system's behavior time series in which the long period parts is the main factor. The acted sequence has good effect of noise immunity.

The AGO/IAGO has a wonderful effect on the processing of some statistical data, e.g. most of the statistical data related to economic growth, crop production, climate and atmospheric changes are mainly affected by long period factors (i.e. low-frequency data), and most of the disturbances are short-period factors (high-frequency data). After processing by the 1-AGO, its high frequency content is suppressed, and its low frequency content is amplified. In terms of information theory, this two-way effect improves the signal-to-noise ratio greatly and reduces the proportion of noise/interference in the new sequence. Based on 1-AGO acting, the information mining and extrapolation prediction will have a good effect.

The authors find that 1-AGO has a wonderful effect on the processing of data sequence. When the 1-AGO acts on a data sequence X, its low-pass filtering effect will benefit the information fluctuations removing and high-frequency noise/interference reduction, so the data shows a clear exponential change trends. However, it is not suitable for excessive use because its equivalent filter has poles at the non-periodic content. But, because of pol effect at zero frequency, the 1-AGO will greatly amplify the low-frequency information parts and suppress the high-frequency parts in the information at the same time.

The purpose of this study is to investigate whether the direct and indirect sentiment measures are similar in explaining mutual fund performance.

The authors examine the role of direct and indirect sentiment measures on fund performance in two scenarios. One is when a sentiment measure is added to market models, and the other is when it used independently. Also, the authors propose a system science theory to explain the findings.

The authors find that both direct and indirect sentiment measures are integral to the benchmark models to explain fund performance. However, while the explanatory power of the direct sentiment index is robust when used independently or collectively, the indirect sentiment measures can explain fund performance only when used along with other market factors.

Given the number of sentiment measures, it is critical to determine whether these measures contain the same information of sentiment. This paper represents the first study on this topic.

The purpose of this study is to examine big data capability's impact on product improvement and explore supply chain dynamics including relationship building and knowledge sharing as important contribution to big data capability.

The research model is tested with survey data. Data analysis results empirically support the proposed model and the hypothesized relationships between the concepts.

First, the hypothesis testing results of this study show that big data capability directly enhances product improvement. Second, this study shows that supply chain relationship building and knowledge sharing are positively related to the development of big data capability.

In supply chain management, there are multiple factors, besides relationship building, that serve as conditioners to knowledge sharing's effect on product performance. We only examined the role of relationship building in this area.

Findings from this research encourage firms to take advantage of their supply chain resources to develop a big data capability that positively contributes to firm performance.

The contribution lies in that it brings to light this step that connects big data capabilities and market and financial performance, which is missing in prior research. This study contributes to the literature by identifying supply chain management activities, more specifically, supply chain relationship building and knowledge sharing, as antecedents to big data capability. This helps to extend this emergent enterprise of big data research to a new area and points to new directions for future research.

The purpose of this paper is to present the terms of grey numbers and its operations.

The definitions of elementary terms about grey numbers and its operations are presented one by one.

The reader could know the basic explanation about the important terms about grey numbers and its operations from this paper.

Many of the colleagues thought that unified definitions of key terms would be beneficial for both the readers and the authors.

It is a fundamental work to standardise all the definitions of terms for a new discipline. It is also propitious to spread the universal principles of grey system theory.

The purpose of this paper is to present the terms of sequence operators and grey data mining.

The definitions of basic terms about sequence operators and grey data mining are presented one by one.

The reader could know the basic explanation about the important terms about sequence operators and grey data mining from this paper.

Many of the colleagues thought that unified definitions of key terms would be beneficial for both the readers and the authors.

It is a fundamental work to standardize all the definitions of terms for a new discipline. It is also propitious to spread the universal principles of grey system theory.