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The purpose of this paper is to provide a new recursive GM (1,1) model based on forgetting factor and apply it to the modern weapon and equipment system.

In order to distinguish the contribution of new and old data to the grey prediction model with new information, the authors add forgetting factor to the objective function. The purpose of the above is to realize the dynamic weighting of new and old modeling data, and to gradually forget the old information. Second, the recursive estimation algorithm of grey prediction model parameters is given, and the new information is added in real time to improve the prediction accuracy of the model.

It is shown that the recursive GM (1,1) model based on forgetting factor can achieve both high effectiveness and high efficiency.

The paper succeeds in proposing a recursive GM (1,1) model based on forgetting factor, which has high accuracy. The model is applied to the field of modern weapon and equipment system and the result the model is better than the GM(1,1) model. The experimental results show the effectiveness and the efficiency of the prosed method.

The purpose of this paper is to construct an unbiased interval grey number prediction model with new information priority for dealing with the jumping errors from difference equation to the differential equation in the prediction model of interval grey number.

First, this study obtains a set of linear equations about the model parameters by taking the minimum error sum of squares between the accumulative sequence and its simulation values as criterion, and solves them on the basis of the Crammer rule. Then, according to the new information priority principle, it selects the last number of the accumulated generation sequence as the initial value and gives the expression of the time response function by the recursive iteration method to establish the interval grey number prediction model.

This paper provides an unbiased interval grey number prediction model with new information priority, and the example analysis shows that the method proposed in this paper has higher prediction precision and practicality.

If there is a better method to whiten the interval grey number, so as to fully tap the grey information contained in it, the accuracy of the model will be higher.

The model proposed in this paper can avoid the error caused by jumping from difference equation to differential equation and make full use of new information. It can be better used in a problem where new information has a great influence on prediction results.

This paper selects the last number of the accumulated generation sequence as the initial value and gives the expression of the time response function by the recursive iteration method. Then, it constructs an unbiased interval grey number prediction model with new information priority.

This paper revolves around the usage of data analytics in the Qur’an and Hadith through a new text mining technique to answer the main research question of whether the activities and the data flows of the Murabaha financing contract is compatible with Sharia law. The purpose of this paper is to provide a thorough and comprehensive database that will be used to examine existing practices in Islamic banks’ and improve compliancy with Islamic financial law (Sharia).

To design a Sharia-compliant Murabaha business process originated on text mining, the authors start by identifying the factors deemed necessary in their text mining techniques of both texts; using a four-step strategy to analyze those text mining analytics; then, they list the three basic approaches in text mining used for new knowledge discovery in databases: the co-occurrence approach based on the recursive co-occurrence algorithm; the machine learning or statistical-based; and the knowledge-based. They identify any variation and association between the Murabaha business processes produced using text mining against the one developed through data collection.

The main finding attained in this paper is to confirm the compatibility of all activities and the data flows in the Murabaha financing contract produced using data analytics of the Quran and Hadith texts against the Murabaha business process that was developed based on data collection. Another key finding is revealing some shortcomings regarding Islamic banks business process compliance with Sharia law.

Given Murabaha as the most popular mode of Islamic financing with more than 75% in total transactions, this research has managed to touch-base on an area that is interesting to the vast majority of those dealing with Islamic finance instruments. By reaching findings that could improve the existing Islamic Murabaha business process and concluding on Sharia compliance of the existing Murabaha business process, this research is quite relevant and could be used in practice as well as in influencing public policy. In fact, Islamic Sharia law experts, Islamic finance professionals and Islamic banks may find the results of this study very useful in improving at least one aspect of the Islamic finance transactions.

By using a novel, fresh text mining methods built on recursive occurrence of synonym words from the Qur’an and Hadith to enrich Islamic finance, this research study can claim to have been the first of its kind in using machine learning to mine the Quran, Hadith and in extracting valuable knowledge to support and consolidate the Islamic financial business processes and make them more compliant with the i.

The purpose of this paper is to solve an initial-value problem for the general fractional differential equation of the nonlinear Lienard's equation.

A new recursive scheme is presented by combining the Adomian decomposition method with a magnificent recurrence formula and via the solutions of the well-known generalized Abel equation.

It is shown that the proposed method may offer advantages in computing the components yn; n = 1; 2; … in an easily computed formula. Also, the numerical experiments show that with few iterations of the recursive method, this technique converges swiftly and accurately.

The approach is original, and a reasonably accurate solution can be achieved with only two components. Moreover, the proposed method can be applied to several nonlinear models in science and engineering.

The philosophical foundations determine how an academic discipline identifies, understands and analyzes phenomena. The choice of philosophical perspective is vital for both marketing and service research. This paper aims to propose a social and systemic perspective that addresses current challenges in service and marketing research by revisiting the philosophy of science debate.

The paper revisits the philosophy of science debate to address the implications of an emergent, complex and adaptive view of marketing and service research. It draws on critical realism by combining structuration and systemic perspectives.

A recursive perspective, drawing on structures and action, is suggested as it includes multiple actors’ intentions and captures underlying drivers of market exchange as a basis for developing marketing and service strategies in practice. This is aligned with other scholars arguing for a more systemic, adaptive and complex view of markets in light of emerging streams in academic marketing and service research, ranging from value cocreation, effectuation, emergence and open source to empirical phenomena such as digitalization, robotization and the growth of international networks.

The reciprocal dynamic between individuals and the overarching system provides a reflexivity approach intrinsic to the service ecosystem. This creates new avenues for research on marketing and service phenomena.

This paper discusses critics, conflicts and conceptualization in service research. It suggests a possible approach for service research and marketing scholars capable of responding to current complexities and turbulence in economic and societal contexts.

The purpose of this paper is to construct an interval grey number NGM(1,1) direct prediction model (abbreviated as IGNGM(1,1)), which need not transform interval grey numbers sequences into real number sequences, and the Markov model is used to optimize residual sequences of IGNGM(1,1) model.

A definition equation of IGNGM(1,1) model is proposed in this paper, and its time response function is solved by recursive iteration method. Next, the optimal weight of development coefficients of two boundaries is obtained by genetic algorithm, which is designed by minimizing the average relative error based on time weighted. In addition to that, the Markov model is used to modify residual sequences.

The interval grey numbers’ sequences can be predicted directly by IGNGM(1,1) model and its residual sequences can be amended by Markov model. A case study shows that the proposed model has higher accuracy in prediction.

Uncertainty and volatility information is widespread in practical applications, and the information can be characterized by interval grey numbers. In this paper, an interval grey numbers direct prediction model is proposed, which provides a method for predicting the uncertainty information in the real world.

The main contribution of this paper is to propose an IGNGM(1,1) model which can realize interval grey numbers prediction without transforming them into real number and solve the optimal weight of integral development coefficient by genetic algorithm so as to avoid the distortion of prediction results. Moreover, the Markov model is used to modify residual sequences to further improve the modeling accuracy.

The purpose of this paper is to provide a theoretical framework for understanding and formulating team intentional change.

Based on a critical review of existing theories of group development, gaps in the literature regarding how teams can and should develop, especially when the change is intentional and has a desired direction, are examined. A set of propositions is offered to address these aspects of group development that have been neglected by the literature.

A systematic and critical discussion of the core literature on group development showed: the complex and discontinuous nature of change in groups was neglected, because the group was not treated as a complex system; the literature has not dealt with group development processes when the change process is intentional (it has been descriptive of what occurs), nor has it examined which are key drivers of group change; existing literature on group development have predominantly seen negative emotion as catalytic to group development, and they have ignored the role of positive emotion. Therefore, drawing on positive psychology, complexity theory, small group research literatures, and Boyatzis' intentional change theory, a prescriptive theoretical framework for explaining intentional group change and development is offered and discussed.

A prescriptive model or theoretical framework with a set of hypotheses are proposed that explain intentional, and positive group development processes.

The purpose of this paper is to study the effects of hedging with options and cardinality constraints in multi‐period portfolio management systems.

The paper focuses on a recursive multi‐period portfolio management formulation (SHAREX) subject to hedging with cardinality constraints and options. The problem formulation is tested with observed and simulated data.

The yield of the multi‐period cardinality constrained option hedging framework under integer‐valued transactions and fixed and variable transactions costs exceeds the riskless return predicted by the Black‐Scholes model in equilibrium.

The paper demonstrates that the multiple representations framework constructed to generate optimal predictions provides accurate forecasts with obvious value for portfolio management.

In this chapter, the authors discuss how visual artifacts may support the analysis and interpretation of qualitative data in organization studies. They draws on their own experience as well as other scholars’ published work to explore the distinctive affordances of visual forms. In particular, the authors identify four roles – namely “mapping,” “analyzing,” “conceptualizing,” and “communicating” – that visual artifacts play to help us move from raw qualitative data to a compelling conceptual product.

Specifically, the use of visuals for “mapping” involves directly coding data into visual forms such as cognitive maps, flow charts or relational diagrams, an approach that may offer a useful complement to traditional verbal coding. Using visuals for “analyzing” implies either comparing, aggregating or decomposing previously constructed visual maps, or drawing directly on verbal data to develop visuals such as analytical flow charts, process replication maps, and trend charts. Using visuals for “conceptualizing” involves rising above the data to develop more abstract representations of concepts and relationships, while maintaining recognizable connections to empirical phenomena. While conceptual models can take a wide variety of forms, the authors illustrate, in particular, the use of visuals to represent linear, dialectic and multi-level process theories. Finally, the authors consider the importance of visualizations for “communicating” insights as well as for developing them, and the inextricable linkages between the two.

The authors conclude by discussing some of the strengths and weaknesses of visualization and by considering how new technologies may offer further possibilities for useful and insightful visual representations of qualitative data that can enhance theory-building.

The purpose of this paper is to inform international student study strategies as well as course design and instructional approach.

Multiple research methods are applied, starting with exploratory data analysis, principal component analysis, confirmatory ordinal factor analysis, then recursive regression.

The meta‐cognitive impacts of international learning styles on academic performance over two courses are proven.

The learning styles of multicultural university students are assessed using an online a priori instrument to determine predictive impact on academic performance across different courses.

The implications of the dendrogram models are briefly explained with respect to student counselling, student study strategies and teaching approaches. The findings are discussed with respect to rival learning style theories and to appease criticisms of meta‐analysis reviews.

Several statistically significant models were created including varimax and promax rotation solutions from ordinal factor analysis, as well as item response and latent factor dendrograms from recursive regression.