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The purpose of this paper is to investigate temporal tweet patterns and their effectiveness in predicting the financial performance of a movie. Specifically, how tweet patterns are formed prior to and after a movie’s release and their usefulness in predicting a movie’s success is explored.

Volume was measured and sentiment analysis was performed on a sample of Tweets posted four days before and after the release of 86 movies. The temporal pattern of tweeting for financially successful movies was compared with those that were financial disappointments. Using temporal tweet patterns, a number of machine learning models were developed and their predictive performance was compared.

Results show that the temporal patterns of tweet volume, length and sentiment differ between “hits” and “busts” in the days surrounding their releases. Compared with “busts” the tweet pattern for “hits” reveal higher volume, shorter length, and more favourable sentiment. Discriminant patterns in social media features occur days in advance of a movie’s release and can be used to develop models for predicting a movie’s success.

Analysis of temporal tweet patterns and their usefulness in predicting box office returns is the main contribution of this research. Results of this research could lead to development of analytical tools allowing motion picture studios to accurately predict and possibly influence the opening night box-office receipts prior to the release of the movie. Also, the specific temporal tweet patterns presented by this work may be applied to problems in other areas of research.

The purpose of this paper is to present a novel approach for digital watermarking and steganography technique that uses neural networks. The performance of the proposed solution in terms of its capacity, transparency, and robustness is investigated.

The proposed technique trains a neural network to learn the perceptual masking model of the human vision system. Once trained, the neural network identifies pixels whose most significant alteration will be least perceptible to the human eye. The image is then altered based on the network recommendation to include the watermark or the covert data.

Experimental results demonstrate that the proposed technique offers excellent transparency and good capacity. In addition, since neural networks store their learned knowledge in a distributed fashion, steganalysis of the image without access to the network is very difficult, if not impossible. Results demonstrate good performance of the proposed solution in terms of its capacity, transparency, and robustness.

Use of neural networks in extracting and representing perceptual masking model of human vision system is interesting. Value added by the proposed approach is in its use of artificial neural networks to model the perceptual masking model of human vision system for injecting imperceptible data into most perceptually significant pits of an image. The proposed approach may be used in combination with most current and popular methods with little impact on perceptual quality of the resulting image.

The purpose of this study is to verify the factors affecting the use of emotional and rational appeals in online advertising among Muslim customers in Iran.

By reviewing the literature of advertising appeals and developing a comprehensive theoretical model, the effect of rational and emotional appeals on online advertising was examined. Expert questionnaire was administered to verify the validity of collected features. The Student's t-test was utilized to analyze the data collected from 271 participants.

Five latent variables, namely user type, product involvement, e-lifestyle, advertising strategies, and internet motives were examined to explain factors affecting online advertising appeals among Muslim customers in Iran. It was found that “advertising strategies” and “user type” are the most effective factors influencing Muslims customers in developing an online advertising campaign.

The sample of this study was Iranian experts and it is necessary to conduct a survey with a larger sample size.

This study provides insights into factors affecting the selection of emotional and rational appeals in Muslims countries. Moreover, it reports the primary columns of online advertising appeals.

This study aims to focus on dual embeddedness as an important channel through which foreign subsidiaries access and share valuable and idiosyncratic knowledge within the multinational corporation (MNC). The authors examine the dual embeddedness challenges of foreign subsidiaries based in the context of Iran as a transitional market.

The final sample includes 144 active foreign subsidiaries in Iran from across a broad range of industries. A structured questionnaire was distributed to firms and structural equation modeling was adopted to analyze the results.

The findings reveal how building external embeddedness in an environment with potentially poor access to valuable knowledge, and risk of knowledge leakage impacts the subsidiary’s ability to subsequently transfer this knowledge within the MNC. The authors identify the significance of absorptive capacity as a way for the subsidiary to access knowledge from and share knowledge with firms in the local market.

Departing from existing work on subsidiary embeddedness in developed markets, the authors reveal how competence creating subsidiaries manage dual embeddedness and knowledge transfer in transition economies that are low in knowledge stocks. The authors unpack how subsidiary absorptive capacity enables access to local knowledge in a transitional market and increases reverse knowledge transfer in the MNC. In doing so, the authors answer calls for work on the dynamic and complementary relationships that exists between subsidiary dual embeddedness, absorptive capacity and knowledge sourcing in less open markets. Focusing on Iran as a transitional economy, this study provides greater contextual nuance to the extant literature on subsidiary dual embeddedness.

This research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.

The authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.

The major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.

This paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.

The findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.

This research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

The purpose of this paper is to employ agility concept to develop a contingency perspective of relationship between suppliers' involvement, absorptive capacity (AC) and product innovation ( PI ). While the moderating effect of AC on the relationship between supplier involvement and PI performance is investigated, a firm's agility in PI is entered as one dimension of the firm's performance to accommodate a multidimensional perspective.

The paper formulates six hypotheses extracted from the relevant literature. The survey was conducted over the internet by using web-based questionnaire. A sampling frame of 1,200 manufacturing UK-based companies provided 233 usable responses. A confirmatory factor analysis was used to test a validity and reliability of constructs and further the paper employed hierarchical multiple regression to test the research hypotheses.

The results while reaffirm some of the existing theories of the subject provide some differing view of the issues allowing projection of new insight on the approach to PI and involvement of suppliers. The results support the proposition of PI performance multidimensionality where achievements beyond financial and market-related factors play a critical role. Furthermore, research findings suggest AC as a competitive factor that can provide the grounds for proactively winning in the PI game through increasing agility capabilities.

This study uses a random sample of UK manufacturing companies, which could be extended to firms from outside the UK too.

The paper provides a new insight into the existing literature on “new product innovation” and its relationship with suppliers' involvement as well as the firm's AC by employing agility perspective, as a leading theory to explain dynamics and uncertainties in the business environment.

In today's rapidly evolving digital era, the focus on Customer Knowledge Management (CKM) has emerged as a critical endeavor for businesses aiming to enhance the customer experience, strengthen customer relationships, and secure future engagement, particularly as they navigate the complex landscape of digital transformation. The integration of innovative and ethical digital transitions has the potential to significantly influence the CKM process, ultimately reshaping the very essence of the business model in terms of performance, productivity, and sustainability within the market. The core objective of this chapter is to explore further the complex domain of digitalization and its profound impact on businesses, with a specific emphasis on Small and Medium-sized Enterprises (SMEs). Through the lens of CKM, we will explore how leveraging digital tools and strategies can empower businesses, especially SMEs, to foster digital innovation within their CKM initiatives and subsequently attain and sustain a competitive advantage in an increasingly dynamic and competitive marketplace.

Silicone softeners are widely used in the textile industry to improve the performance of textile products. The thermal characteristics and flammability of polyester fabrics can be influenced by these compounds, which need to be considered, as important issues of human safety. The purpose of this paper is to investigate the changes induced on the polyester fibre by silicone softener treatment using a pad/dry/cure method.

The fibres were first treated with nano‐ and microemulsion silicone softeners. The influence of the silicone emulsion type on thermal properties and flammability of the resultant samples were investigated by various analytical techniques, namely, the differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA), dynamical mechanical thermal analysis (DMTA) and horizontal flammability test (HFT).

Results showed that the silicone softeners increase the thermal degradation and flammability of the polyethylene terephthalate (PET) substrate.

The paper's study of thermal and flammability of the silicone‐treated sample is novel and can be used to optimize the properties of silicone polymers during production and consumption.

Proper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.

The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.

The performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.

The proposed methodology can be applied to find the best response plan for all crises.

In this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.

In this study, for the first time, the efficacy of control rods for full suppression of vortex-induced vibrations (VIV) and galloping of an elastically supported rigid square cylinder that vibrates freely in the cross-flow direction is investigated.

To this aim, two small control rods are placed at constant angles of ± 45° relative to the horizontal axis and then the influence of diameter and spacing ratios on the oscillation and hydrodynamic response along with the vortex structure behind the cylinder is evaluated in the form of nine different cases in both VIV and galloping regions.

The performed simulations show that using the configuration presented in this study results in full VIV suppression for the spacing ratios G/D = 0.5, 1 and 1.5 at the diameter ratios d/D = 0.1, 0.2 and 0.3 (D: diameter of square cylinder, G: distance between rods and cylinder, d: diameter of rods). On the contrary, a perfect attenuation of galloping is only achieved at the largest diameter (d/D = 0.3) and the smallest spacing ratio (G/D = 0.5). In general, for both VIV and galloping regions, with increasing diameter ratio and decreasing spacing ratio, the effect of the control rods wake in the vortex street of square cylinder gradually increases. This trend carries on to the point where the vortex shedding is completely suppressed and only the symmetric wake of control rods is observed.

So far, the effect of rod control on VIV of a square cylinder and its amplitude of oscillations has not been investigated.