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Construction projects in India continue to suffer excessive delays. This paper aims to investigate the delay causes in India by project sector (transport, power, buildings and water) and carry out a comparative study of delay causes in design build (DB) projects with that of design bid build (DBB) projects along with mitigation measures.

A questionnaire survey was conducted among major clients, contractors and consultants in India. Importance Index was used for ranking of the delay causes. Projects were categorized based on the type, and causes of delay in each project type were identified. Projects were also categorized based on type of contract, and delay causes in DB and DBB projects were compared. Statistical analysis of responses by Cronbach’s alpha, one-way analysis of variance, Kruskal–Wallis tests was carried out with Statistical Package for Social Sciences. Semi-structured in-depth interviews were conducted with senior industry professionals to develop exhaustive mitigation measures.

The research findings indicate finance-related causes as the most critical causes of delay in Indian projects. Delay in settlement of claims, contractor’s financial difficulties, delay in payment for extra work/variations by owner, late payment from contractor to subcontractor or suppliers, variation orders/changes of scope by owner during construction and changes in design by owner were the highly ranked delay causes. The research found no significant difference in the delay causes in DB and DBB projects.

This is the first study wherein delay causes for various project types within a single country are identified. In addition, the study has identified and compared the delay causes in DB projects and DBB projects. Mitigation measures developed in this study will help professionals and project managers not just in India but other developing countries as well to alleviate delay causes and in improvement of project timelines.

The main purpose of this research is to find an optimal allocation of marketing budgets which maximizes customer equity in an uncertain environment. Since markets are naturally uncertain environments, the aim is to incorporate uncertainty into the model.

Researchers have developed a mathematical programming model which employs customer equity as an objective function in order to allocate marketing budgets. The robust optimization approach is employed to tackle the proposed model, which deals with uncertainty.

The solution of the robust model is shown to be feasible and satisfactory in all uncertain situations. The robust solutions (of the presented model) are stable in volatile situations; while if the solution of deterministic models is used, it may be suboptimal or even infeasible. Sensitivity analysis of the deterministic solution only describes how stable is the suggested solution, but a robust optimization approach always provides a stable solution.

The presented model will be most effective where uncertainty is high; if uncertainty is not a matter of concern or estimates are reliable, deterministic models are also effective.

Companies periodically decide on marketing budgets in order to achieve predefined marketing targets in future periods. The results of this research may be useful and applicable in marketing departments for allocating marketing budgets, especially in uncertain environments.

The main contribution of this research lies in providing an approach to allocate marketing budgets in uncertain environments. Unlike previous studies, the presented method takes into account the uncertainty of parameters in a systematic way. Hence, in case of high degrees of uncertainty, the use of robust optimization is strictly recommended.

The purpose of this paper is to present a model that retailers engaged in e‐commerce (e‐tailers) can use for determining the optimal mix of customer segments within a customer portfolio from an integrated risk and return perspective.

Portfolio Selection Theory of Markowitz is applied to find the optimal composition of customer portfolios. The model is developed and discussed for two customer segments (relationship‐ and transaction‐oriented customers) and exemplarily applied to a data set of an e‐tailer.

Portfolio Selection Theory of Markowitz is well‐suited and promising for determining an optimal customer portfolio from a risk‐return perspective. However, since customers vary from financial assets in several aspects, the results of the model have to be interpreted conscientiously and the resulting action options have to be interpreted within the context of customer relationship management (CRM).

The model proposes to carry out a sequential set of one‐period optimizations. To reduce complexity, several simplifying assumptions were made within the model regarding the characteristics of customer segments and portfolio as well as the expected risk and return.

A current survey among German companies indicates that companies already have broad experiences in customer evaluation. However, it also turned out that evaluating customers' potential and risk simultaneously is still a major challenge. Our new approach facilitates the making of sound investment decisions into single customer relationships with respect to an overall optimal customer portfolio. Thus, a formal link to value‐based management is established.

Using CRM for a value‐based management of customer portfolio according to a superordinated risk management objective has so far received little attention in literature. This paper's model is a new approach in customer portfolio management for e‐tailers taking customers' risk and return characteristics simultaneously and in real‐time into consideration.

This study aims to identify, within the context of the French fashion industry, the characteristics of multichannel shoppers, that is, consumers who use more than one channel in a single shopping trip. We especially investigate whether consumers' focus on quality versus price affects their multichannel shopping tendency and their flexibilities in their shopping lists (basket flexibility).

We surveyed a representative sample of 400 French shoppers regarding fashion apparel purchasing. We use a logistic regression framework to measure the probability of a shopper becoming a multichannel shopper based on the key constructs and a battery of control variables.

The analysis shows that, in fashion buying, shoppers focused on quality and those with high basket flexibility have a higher probability of becoming multichannel shoppers. The probability becomes even greater when a shopper is both quality oriented and has basket flexibility.

We focus on the fashion apparel market for a deeper understanding of multichannel usage of products with both experience and search features. Future research can investigate other industries for higher generalizability.

Our research provides insights into multichannel fashion companies whose managements aim to effectively manage high-value customers who tend to use more channels when shopping. Specifically, an omnichannel marketing strategy should focus on capturing the quality-oriented and highly basket-flexible segment of consumers.

Our study provides evidence that for products having high experiential as well as search features, quality-oriented and highly flexible shoppers engage more in multichannel shopping. Because these characteristics are related to the long-term value of customers, we provide the link between multichannel marketing and firm profitability in the context of the fashion industry.

The last two decades have seen a surge of interest in the concept of value in business markets. Furthermore, extant literature suggests that value capture can be conceptualized as the return on the firm's customer assets. However, the existing customer asset management literature has a strong bias towards consumer markets. Thus, the purpose of this paper is to create a conceptual framework for managing customer assets for improved value capture in a business market context, and to illustrate the use of the framework empirically.

The authors approach the topic with conceptual development and a longitudinal case illustration from a globally operating forestry product firm.

The findings of the study indicate that B2B firms can increase their value capture by dividing their customer base into customer portfolios, which are managed with differentiated customer management concepts targeted to increase the economic profit contribution of each customer portfolio.

The business practitioners in B2B contexts are likely to find the proposed customer portfolio approach to managing the customer assets more approachable than the prevailing customer lifetime models. In order to gain maximum value capture benefits from portfolio-specific customer management concepts, they should be approached cross-functionally instead of limiting them to the domains of marketing and sales.

The study contributes to literature on value capture and customer asset management by providing a framework for managing customer assets for increased value capture that is applicable to business markets and circumvents the majority of challenges associated with the customer lifetime value models.

In this invited paper, the authors aim to offer an integrated marketing communications (IMC) framework for understanding how disparate customer touchpoints impact consumer engagement and profitability in an omni-channel environment. For each aspect of the framework, the authors recommend areas for further research.

The authors review literature linking personal and electronic channels of communication in an omni-channel context to consumer engagement, with an emphasis on channel and message unity.

Five major research areas were identified: research that better links omni-channel and IMC theory and practice; conceptual and empirical research that helps operationalize the consumer-brand engagement construct, including its antecedents and consequences; Build understanding of off- and on-line consumer-brand touchpoints and how they may enhance engagement and profitability; how omni-channel IMC best monetizes buyer–seller relationships; and omni-channel IMC in other consumer decision contexts.

The emergence of omni-channel marketing is breaking down the silos across available consumer-brand touchpoints. The intersection of effective omni-channel marketing and IMC strategic and tactical initiatives offers marketers an opportunity to engage their customers and to form profitable relationships.

The authors proposed an omni-channel IMC Framework and a research agenda for advancing the field. As this is a new area of inquiry, the authors argue for the development of other comprehensive frameworks, both for general omni-channel IMC conceptualizations.

Recent multichannel research suggests that consumers use multiple channels to reap attribute-based benefits which have led to showrooming phenomenon. The purpose of this paper is to investigate the reasons for consumers’ showrooming behaviour and propose a comprehensive model based on application and extension of the “Theory of planned behaviour”.

Using the probability sampling approach, 278 complete responses were obtained via web-based surveys for analysing the showrooming behaviour. The research model was tested using the “Partial least squares method” which follows a variance-based structural equation modelling approach.

The results of the study indicate that “touching and feeling the product” and “sales staff assistance” motivated customers to visit the physical store before buying online. “Better online service quality” and “lower prices online” induced customers to later purchase online. Price conscious customers and those with the ability to use multiple channels were more likely to engage in showrooming behaviour.

The generalization of the findings may be limited because the data were collected from a small sample size. The subject calls for more extensive research for drawing generalizations due to lack of the substantive literature on the core area of study.

The model proposed will help retailers in understanding the showrooming phenomenon which recent researchers have considered as a threat to retail. The study provides basis for devising strategies to defend showrooming customers.

This paper adds to the body of knowledge in retailing by proposing a model on showrooming which is an emerging area of research in the present retail landscape.

Psychological perspective has been omitted or considered a secondary issue by past studies focused on e-commerce recommendation systems (RS). However, this perspective is key to gaining a better understanding of consumer behaviours when these systems are used to support purchasing processes at online stores. The paper aims to discuss these issues.

The field study consisted of a simulated online shopping process undertaken by a sample of internet users with a recommender system at a real online store (Pixmania). The authors applied rigorous and detailed exploratory and confirmatory factor analyses to assess the empirical validity of the model.

The proposed sequence of psychological outcomes is valid, with the exception of one hypothesized relationship. In particular, satisfaction with an online store’s recommender has a strong influence on a consumer’s willingness to purchase one of the items related to his/her shopping goal. However, this satisfaction has no direct effect on a consumer’s intention to make add-on purchases based on the recommender’s suggestions. On the contrary, the results support the idea that add-on purchases are conditioned by a previous purchase related to the consumer’s initial shopping goal. On the other hand, a consumer’s flow state while shopping improves all his/her psychological outcomes linked to an online store’s recommender. The influence of flow state is particularly interesting when seeking to gain a better understanding of consumers’ unplanned purchases based on the recommender’s suggestions. These findings have important implications for practitioners.

This paper discusses in detail and empirically test a set of psychological outcomes that emerge when an e-vendor’s recommender is used to assist a consumer’s shopping process. To the best of the knowledge, this is the first attempt that empirically tests most of the hypothesized relationships within an online store’s RS context.

The purpose of this paper is to explore the effects of online recommender systems (RS) on three types of diversity: algorithmic recommendation diversity, perceived recommendation diversity and sales diversity. The analysis distinguishes different recommendation algorithms and shows whether user perceptions match the actual effects of RS on sales.

An online experiment was conducted using a realistic shop design, various recommendation algorithms and a representative consumer sample to ensure the generalizability of the findings.

Recommendation algorithms show a differential impact on sales diversity, but only collaborative filtering can lead to higher sales diversity. However, some of these effects are subject to how much information firms have about users’ preferences. The level of recommendation diversity perceived by users does not always reflect the factual diversity effects.

Recommendation and consumption patterns might differ for other types of products; future studies should replicate the study with search or credence goods. The authors also recommend that future research should move from taking a unidimensional measure for the assessment of diversity and employ multidimensional measures instead.

Online shops need to conduct a more comprehensive assessment of their RS’ effect on diversity, taking into account not only the effects on their sales distribution, but also on users’ perceptions and faith in the recommendation algorithm.

This study offers a framework for assessing different forms of diversity in online RS. It employs various recommendation algorithms and compares their impact using not just one but three different types of diversity measures. This helps explaining some of the contradictious findings from the previous literature.

The purpose of this paper is to apply the Steady State Kalman Filter for temperature measurements of tissues via magnetic resonance thermometry. Instead of using classical direct inversion, a methodology is proposed that couples the magnetic resonance thermometry with the bioheat transfer problem and the local temperatures can be identified through the solution of a state estimation problem.

Heat transfer in the tissues is given by Pennes’ bioheat transfer model, while the Proton Resonance Frequency (PRF)-Shift technique is used for the magnetic resonance thermometry. The problem of measuring the transient temperature field of tissues is recast as a state estimation problem and is solved through the Steady-State Kalman filter. Noisy synthetic measurements are used for testing the proposed methodology.

The proposed approach is more accurate for recovering the local transient temperatures from the noisy PRF-Shift measurements than the direct data inversion. The methodology used here can be applied in real time due to the reduced computational cost. Idealized test cases are examined that include the actual geometry of a forearm.

The solution of the state estimation problem recovers the temperature variations in the region more accurately than the direct inversion. Besides that, the estimation of the temperature field in the region was possible with the solution of the state estimation problem via the Steady-State Kalman filter, but not with the direct inversion.

The recursive equations of the Steady-State Kalman filter can be calculated in computational times smaller than the supposed physical times, thus demonstrating that the present approach can be used for real-time applications, such as in control of the heating source in the hyperthermia treatment of cancer.

The original and novel contributions of the manuscript include: formulation of the PRF-Shift thermometry as a state estimation problem, which results in reduced uncertainties of the temperature variation as compared to the classical direct inversion; estimation of the actual temperature in the region with the solution of the state estimation problem, which is not possible with the direct inversion that is limited to the identification of the temperature variation; solution of the state estimation problem with the Steady-State Kalman filter, which allows for fast computations and real-time calculations.