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This study clarifies the integration-related effects of photos and text on consumer information processing and decision-making outcomes.

The authors conducted an experiment by recruiting 162 workers from Amazon Mechanical Turk. These participants were randomly assigned based on a full factorial, between-subject design with four possible conditions (2 [separate vs alternate layout] × 2 [photo-first vs text-first sequence]). The authors conducted a two-way analysis of variance to test the main effects and the interaction effects of layout and sequence on perceived diagnosticity, pleasantness feelings and attitudes toward products or services reviewed through electronic word-of-mouth (e-WOM); the authors also applied Process Models 4 and 8 to explore the mechanism of these effects.

The experimental results reveal that text-first sequence is generally more effective than photo-first sequence in enhancing perceived diagnosticity and attitudes toward products or services. However, when a photo is displayed first, a separate layout is more effective than an alternate layout in enhancing perceived diagnosticity and attitudes. By contrast, regardless of the sequence, an alternate layout is more effective than a separate layout in inducing pleasantness feeling.

Future studies should further explore photo-based e-WOM, including other photo characteristics (e.g. visual quality, quantity and content).

This study provides guidelines for businesses to use photos on social media to achieve strategic goals.

This study addresses an identified need; that is, how the presentation of photo cues (e.g. layout and sequence) influences consumer decisions.

Smog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction.

This paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness.

In order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies.

The proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective.

Based on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model.

Aiming at the traditional multivariate grey forecasting model only considers the modelling of real numbers; therefore, the purpose of this paper is to construct an MGM(1, m) model based on the interval grey number sequences according to the grey modelling theory.

First, the multivariable grey number sequences are transformed into the kernel and grey radius sequences which are two feature sequences of interval grey number sequences. Then the MGM(1, m) model for kernel sequences and grey radius sequences are established, respectively. Finally, the simulation and prediction of the upper and lower bounds of the interval grey number sequences are realized by the reductive calculation of the predicted values of the kernel and grey radius.

The model is applied to the prediction of visibility and relative humidity, the identification factors of the haze. The results show that the model has high accuracy on the simulation and prediction of multivariable grey number sequences, which is reasonable and practical.

The main contribution of this paper is to propose a method to simulate and forecast the multivariable grey number sequence that is to establish the prediction models for the whitening sequences of multivariable grey number sequences which are kernel and grey radius sequences and extend the possibility boundary of kernel by grey radius. The model can reflect the development trend of multivariable grey number sequence accurately. When the grey information is continuously complemented, the multivariable grey number prediction model is transformed into the traditional MGM(1, m) model. Therefore, the MGM(1, m) model based on interval grey number sequence is the generalisation and expansion of the traditional MGM(1, m) model.

Based on literature review, the purpose of this paper is to design a comprehensive evaluation system for pharmaceutical industry from the financial perspective, and then analyses the development strategy of Chinese pharmaceutical industry according to the data of listed companies.

This paper analyses the development strategy of Chinese pharmaceutical industry through building the grey dominance evaluation model including absolute degree of grey incidence model and relative degree of grey incidence model.

Through the analysis of the grey dominance evaluation model, the authors find that four factors including total assets turnover, quick ratio, inventory turnover and current ratio can be grouped into first grade assessment indicator when evaluating the performance of Chinese pharmaceutical industry. These four indexes contain the concept of operational efficiency which shows that operation capability is the key to support the development of Chinese pharmaceutical industry, needing to be highly valued when organizations making business policy. When it comes to velocity of development, the contribution of R & D intensity is relatively small, which shows innovation ability of China’s pharmaceutical industry is still weak. Innovation is the lifeblood of enterprise development, needing to be improved to promote enterprise’s core competitiveness in the future development.

This paper selects Y1-Y7 as the performance evaluation system of pharmaceutical manufacturing enterprises, while X1-X15 as performance evaluation index system from the financial perspective, which indicated that the evaluation system is scientific and practical. The empirical result shows that the operation capability makes the largest contribution to the performance of China’s pharmaceutical industry, while R & D ability and the enterprise core competitive ability are weak.

The purpose of this paper is to discuss the necessary condition of the relative error between continuous function transformation after inverse transformation and original sequence is not larger than the relative error between transformed sequence and its corresponding simulation sequence.

First, explore the function transformation feature of after inverse transformation the relative error not enlarged, then combine this feature with the function transformation feature of not enlarge the class ratio dispersion, not reduce the smoothness which author have got, and obtain a kind of special transformation that not enlarge class ratio dispersion, not reduce the smoothness and after inverse transformation keep the relative not enlarged. Meanwhile, offer the concrete form of this special function type to monotone increasing continuous function transformation and monotone decreasing continuous function transformation, respectively, and study its properties.

This paper finds the concise and important feature of monotonically increasing function transformation after inverse transformation whether the relative error enlarge or not is at first, the concise and important feature of monotonically decreasing function transformation after inverse transformation relative error not enlarged is. And find that the ideal function transformation which both reduces class ratio dispersion strictly and keeps error of inverse transformation not enlarged is non-exist for monotone increasing function transformation and monotone decreasing function transformation.

Use the necessary condition given by this paper, it may use to judge whether function transformation can keep relative error of inverse transformation not enlarged by easy data calculation before build modeling, therefore, choose the best function transformation. These results tell the authors: the paper cannot treat any functions as the same that whether the relative error of inverse transformation will not enlarge (or not reduced), but the authors should divide them into two parts to discuss that it will be expanded in some range or be reduced in some range. It will affect the future direction of the research, not to find the function transform both satisfies the class ratio dispersion reduced and keep the relative error of inverse transformation not enlarged, but to study which kind of function transform will narrow class ratio dispersion in some range, after the modeling accuracy improvement, but after the inverse transformation the relative error enlarged, and at this time the simulation accuracy is still higher than the simulation accuracy of original data modeling directly. Which kind of function transform will expand class ratio dispersion in some range, after the modeling accuracy diminution, but after the inverse transformation the relative error not enlarged, and now the simulation accuracy is still higher than the simulation accuracy of original data modeling directly, too.

Let peers no longer spend energy in seeking the function transformation which both reduce class ratio dispersion and keep relative error of inverse transformation not enlarged. At the same time, also remind peers that even if a function transformation reduces class ratio dispersion greatly, the data modeling accuracy improves a lot after transformation, but the error of inverse transformation is may quite large, still. Besides, even if function transformation increases class ratio dispersion, the data modeling accuracy is not good after transformation, the ideal situation after inverse transformation would occur, and the possibility cannot be excluded.

Understanding how dyadic negotiations and group decision processes evolve over time requires specifying the basic elements of process, modeling the configuration of those elements over time, and providing a theoretical explanation for that configuration. We propose a bead metaphor for conceptualizing the basic elements of the group negotiation process and then “string” the beads of behavior in a helix framework to model the process by which group negotiations evolve. Our theorizing draws on the group decision development literature (e.g. Bales, 1953; Poole, 1981, 1983a, b; Poole & Roth, 1989a, b) as well as on the negotiation process literature (e.g. Gulliver, 1979; Morley & Stephenson, 1977). Our examples are from our Towers Market studies of negotiating groups.

Web mining is one of the mining technologies, which applies data mining techniques in large amount of web data to improve the web services. Web traversal pattern mining discovers most of the users’ access patterns from web logs. This information can provide the navigation suggestions for web users such that appropriate actions can be adopted. However, the web data will grow rapidly in the short time, and some of the web data may be antiquated. The user behaviors may be changed when the new web data is inserted into and the old web data is deleted from web logs. Besides, it is considerably difficult to select a perfect minimum support threshold during the mining process to find the interesting rules. Even though the experienced experts, they also cannot determine the appropriate minimum support. Thus, we must constantly adjust the minimum support until the satisfactory mining results can be found. The essences of incremental or interactive data mining are that we can use the previous mining results to reduce the unnecessary processes when the minimum support is changed or web logs are updated. In this paper, we propose efficient incremental and interactive data mining algorithms to discover web traversal patterns and make the mining results to satisfy the users’ requirements. The experimental results show that our algorithms are more efficient than the other approaches.

Examines the relative effects of alternative approaches to dealing with the intractable problem of managing a job shop. The effect of routing flexibility, simple order release mechanisms based on aggregate shop load, and local job prioritizing rules are examined together. While the impact of each experimental parameter is found to be significant, the impact of flexibility greatly overshadows those of the other parameters. These results support further examination and more normative understanding of how flexibility and better production planning and control may best be used in various competitive situations.

The purpose of the paper is to review the issues involved in determining the appropriate speed of adjustment and the sequencing of economic reforms, and to develop a checklist of key guidelines for policymakers as a basis for their decision‐making process.

The paper develops a conceptual framework based on a survey of the theoretical and empirical literature, and the practical experience of the authors in this area.

The analysis in the paper shows that the optimal speed and sequence of reforms is country‐specific. But key policy considerations can help guide policymakers in the design of their reform strategy.

The arguments favoring a shock approach or a gradual approach are not absolute. Each country has to choose the proper speed of adjustment and sequencing of reforms by examining country‐specific factors. A thorough case‐by‐case analysis is needed before a decision on the appropriate timing and sequencing of reforms can be made.

The analysis in the paper leads to key reform guidelines for policymakers – covering areas such as prerequisites and resource constraints, political economy considerations, credibility and sustainability of reforms – that are instrumental in developing a well‐sequenced strategy.

The purpose of this paper is to analyze the key influencing factors which affect the performance of M & A and the key influencing factors by establishing grey relational sequence model. The results can not only perfect the current existing theoretical system, but also provide theoretical guide for M & A practice.

First of all, this paper analyzes the M & A performance through the literature reviews, and then establishes the grey model about M & A performance from three influencing factors: profitability, operational and development capacity to analyze its key influencing factors which affect M & A performance. Furthermore, the key factors influencing of M & A performance are analyzed, the results can be obtained through the analysis of the grey relative degree based on six years’ data of state-owned enterprises.

In this paper, some results can be found that ownership restriction and executive compensation can significantly affect corporate profitability and operation ability. M & A and institutional investors can be affected greater from the perspective of development ability and profitability. In general, enterprise scale has little affect on the performance after M & A.

With the sustainable development of economic globalization, M & A has become an important strategy for the rapid growth of the enterprise, enlargement market and brand effect. But the performance after M & A in most enterprises does not improve. As the pillar of the national economy, state-owned enterprises have an important impact on China’s economic development and urgently need to be reformed.

This paper analyzes the key factors through establishing grey relative sequence model to analyze the correlation of M & A performance and influencing factors, and finds key influencing factors which affect the performance of M & A. The conclusion can provide policy guidance for the improvement of M & A performance in the future.