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The purpose of this paper is to close the gap between the theoretical nature of existing contributions in customer engagement value (CEV) and its need to practically empower business decisions. This is done by proposing a framework that consists of three techniques, each of which combines the components of CEV to make it more comprehensive and applicable. The paper also reviews and analyzes the work that has been done so far in the area of CEV whether in business to business (B2B), business to consumer (B2C) or consumer to consumer (C2C) markets.

CEV is a comprehensive term that measures the total value of the customer through capturing his transactional and non-transactional behaviors. Hence, it is an essential term for measuring the value of the customer in direct marketing. This motivates researchers to compete in developing models to maximize CEV. Meanwhile, most of the existing models are conceptual and the majority of them lack applicability due to many reasons. First, these models relied on a linear version of the CEV model, hence double-counting the value of the customer; also they weighted the components of CEV equally, which is unrealistic. Finally, the effect of the environmental components in determining the engagement level of each customer was almost ignored. In this paper, two main contributions are presented. First, a summary and analysis of the contributions of the literature in the CEV field for different market types whether in B2C, B2B or C2C. Furthermore, three modifications are added to the existing models. The first model introduces a non-linear relationship of the components of CEV. The second model is a weighted linear model of these components. Finally, the third model adds the environmental factors to the CEV components. All the proposed models are theoretical in nature, however, these models are expected to show superiority when being applied to real data sets due to their ability to capture the complexity in the relationship between the firm and its customers in real-life situations. The proposed models are expected to attract the practitioners and other researchers and they both are encouraged to apply the proposed models on real-life data sets, test their performance, compare them against each other, to be able to apply each of them on the best suitable data set and business scenario.

Based on the review and analysis that has been done on about 87 papers, it is found that the majority of the contributions that have been done in the area of CEV are theoretical in nature, in spite of the effectiveness of CEV in empowering business decision. It is also found that few researchers proposed a set of theoretical comprehensive frameworks that combined CEV’s components together. Meanwhile, those frameworks are not practically applicable.

Although the contribution of the proposed models expected to attract both researchers and practitioners, these are not applied to real-life case studies to prove their effectiveness.

The research in this paper has many industrial and managerial implications. First, it helps managers and decision takers to treat the customers as assets and cost-free resources who can work with the firm to achieve what’s both aims to (i.e. increase customer satisfaction and firm’s profitability). Second, it helps the firm to determine the total value of each customer and treat its customers accordingly. Third, it empowers the managers to do target marketing, based on grouping the customers upon their total engagement. This would save time and cost and for sure increase the profitability and customer satisfaction. Forth, the proposed models take into consideration not only the transactional behavior of the customers but also the non-transactional factors that play a significant role in formulating the relationship between the firm and its customers.

This is hereby to certify that the paper is original, neither the paper nor a part of it is under consideration for publication anywhere else. Also, this study has no conflicts of interest to disclose.

The purpose of this paper is twofold. First, the author proposes a financial engineering framework to model commodity prices based on market demand processes and demand functions. This framework explains the relation between demand, volatility and the leverage effect of commodities. It is also shown how the proposed framework can be used to price derivatives on commodity prices. Second, the author estimates the model parameters for agricultural commodities and discuss the implications of the results on derivative prices. In particular, the author see how leverage effect (or inverse leverage effect) is related to market demand.

This paper uses a power demand function along with the Cox, Ingersoll and Ross mean-reverting process to find the price process of commodities. Then by using the Ito theorem the constant elastic volatility (CEV) model is derived for the market prices. The partial differential equation that the dynamics of derivative prices satisfy is found and, by the Feynman-Kac theorem, the market derivative prices are provided within a Monte-Carlo simulation framework. Finally, by using a maximum likelihood estimator, the parameters of the CEV model for the agricultural commodity prices are found.

The results of this paper show that derivative prices on commodities are heavily affected by the elasticity of volatility and, consequently, by market demand elasticity. The empirical results show that different groups of agricultural commodities have different values of demand and volatility elasticity.

The results of this paper can be used by practitioners to price derivatives on commodity prices and by insurance companies to better price insurance contracts. As in many countries agricultural insurances are subsidised by the government, the results of this paper are useful for setting more efficient policies.

Approaches that use the methodology of financial engineering to model agricultural prices and compute the derivative prices are rather new within the literature and still need to be developed for further applications.

How do stock prices evolve over time? The standard assumption of geometric Brownian motion, questionable as it has been right along, is even more doubtful in light of the recent stock market crash and the subsequent prices of U.S. index options. With the development of rich and deep markets in these options, it is now possible to use options prices to make inferences about the risk-neutral stochastic process governing the underlying index. We compare the ability of models including Black–Scholes, naïve volatility smile predictions of traders, constant elasticity of variance, displaced diffusion, jump diffusion, stochastic volatility, and implied binomial trees to explain otherwise identical observed option prices that differ by strike prices, times-to-expiration, or times. The latter amounts to examining predictions of future implied volatilities.

Certain naïve predictive models used by traders seem to perform best, although some academic models are not far behind. We find that the better-performing models all incorporate the negative correlation between index level and volatility. Further improvements to the models seem to require predicting the future at-the-money implied volatility. However, an “efficient markets result” makes these forecasts difficult, and improvements to the option-pricing models might then be limited.

The Navier‐Stokes equation and the species continuity equation have been solved numerically in a boundary fitted coordinate system comprising the geometry of a large scale industrial size tundish. The solution of the species continuity equation predicts the time evolution of the concentration of a tracer at the outlets of a six strand billet caster tundish. The numerical prediction of the tracer concentration has been made with six different turbulence models (the standard k‐ε, the k‐ε RNG, the Low Re number Lam‐Bremhorst model, the Chen‐Kim high Re number model (CK), the Chen‐Kim low Re number model (CKL) and the simplest constant effective viscosity model (CEV)) which favorably compares with that of the experimental observation for a single strand bare tundish. It has been found that the overall comparison of the k‐ε model, the RNG, the Lam‐Bremhorst and the CK model is much better than the CKL model and the CEV model as far as gross quantities like the mean residence time and the ratio of mixed to dead volume are concerned. However, the k‐ε model predicts the closest value to the experimental observation compared to all other models. The prediction of the transient behavior of the tracer is best done by the Lam‐Bremhorst model and then by the RNG model, but these models do not predict the gross quantities that accurately like the k‐ε model for a single strand bare tundish. With the help of the above six turbulence models mixing parameters such as the ratio of mix to dead volume and the mean residence time were computed for the six strand tundish for different outlet positions, height of advanced pouring box (APB) and shroud immersion depth. It was found that three turbulence models show a peak value in the ratio of mix to dead volume when the outlets were placed at 200 mm away from the wall. An APB was put on the bottom of the tundish surrounding the inlet jet when the outlets were kept at 200 mm away from the wall. It was also found that there exists an optimum height of the APB where the ratio of mix to dead volume and the mean residence time attain further peak values signifying better mixing in the tundish. At this optimum height of the APB, the shroud immersion depth was made to change from 0 to 400 mm. It was also observed that there exists an optimum immersion depth of the shroud where the ratio of mix to dead volume still attains another peak signifying still better mixing. However, all the turbulence models do not predict the same optimum height of the APB and the same shroud immersion depth as the optimum depth. The optimum height of the APB and the shroud immersion depth were decided when two or more turbulence models predict the same values.

In this chapter we develop and implement a method for maximum simulated likelihood estimation of the continuous time stochastic volatility model with the constant elasticity of volatility. The approach does not require observations on option prices, nor volatility. To integrate out latent volatility from the joint density of return and volatility, a modified efficient importance sampling technique is used after the continuous time model is approximated using the Euler–Maruyama scheme. The Monte Carlo studies show that the method works well and the empirical applications illustrate usefulness of the method. Empirical results provide strong evidence against the Heston model.

The payoffs of exotic options (e.g., up‐and‐out call options) are dependent on the time‐path of asset prices rather than the price of the asset at a fixed point in time. The authors of this article compare various models for calibrating volatility surfaces in order to price up‐and‐out call options.

The purpose of this paper is to examine the relationship between the ethical culture of organisations and organisational innovativeness.

A quantitative empirical analysis was conducted on the basis of a survey of 147 respondents within the public sector in Finland. A multivariate linear regression analysis was done to examine how the ethical culture of organisations is related to organisational innovativeness.

A positive link was found in the ethical culture of an organisation and organisational innovativeness: ethical culture was important to behavioural, strategic and process innovativeness. Within the ethical culture of an organisation, the dimension of the congruency of management in particular had an important role in organisational innovativeness.

The data were collected from the public sector, and therefore, future studies from the private sector organisations are needed. The results lend support to previous research arguments for the positive effect of an ethical organisational culture on organisational outcomes, particularly the organisational innovativeness described in this paper.

It is suggested that congruency of management, discussability and supportability are the organisational virtues which can most effectively enhance organisational innovativeness, specifically behavioural, strategic and process innovativeness in practice.

The research paper provides empirical evidence on the interrelation between the ethical culture of organisations and organisational innovativeness; evidence which is scarce in existing literature on organisational innovativeness. Thus, the paper helps fill this gap in the literature in the field.

The purpose is to open the possibility for a research institute, perhaps in partnership with a local council and a major developer, to bring together skills necessary to prototype the CEV development model.

This paper advances the development of a hypothetical, systems-based approach to the design and development of smart rural villages – a network of circular economy villages (CEVs). The method is to assimilate visionary ideas from 20th century town planning literature related to decentralisation and the development of new towns in rural areas, identifying key design principles. The present trajectory of infrastructure design and emerging development models are then analysed to modernise the design principles for implementation in the 21st century.

The availability of localised, renewable energy micro-grids potentially makes CEVs feasible and affordable. The shift to remote work and movement of people to regional areas suggests that this may be a desirable development form. This can only be confirmed through the development of a pilot project as proof of concept.

The proposed CEV development model applies circular economy strategies to every aspect of the smart rural village development including financing, ownership, spatial planning, design and material selection.

“Relationship benefits” (RBs) is an approach in relationship marketing. The concept highlights that both customer and firm must receive benefits from the relationship to establish and maintain it. This study aims to identify the impacts of three types of RBs on creating four kinds of customer engagement value (CEVs).

This study synthesizes previous findings and proposes hypotheses with theoretical supports and reports results from a structural equation model that uses data gathered from 577 Iranian customers across a range of services- based on an extensive review of marketing literature related to RBs.

Confidence benefits are the strongest driver of customer lifetime value and customer influence value, while special treatment benefits are the strongest driver of customer knowledge value (CKV) and customer referral value. Social benefits only affect CKV.

Future research should examine the role of other types of RBs in creating CEV, beyond the original three types of RBs.

There is no research addressing the impact of delivering RBs on CEV. This study combines RBs and CEV into a single model and demonstrates the roles of different types of RBs in creating CEV for service firms.