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Within the framework of the theory of planned behavior (TPB), this study aims to investigate which factors affect consumers’ purchase intention with regard to over-the-counters (OTCs), focusing on personal factors, including health consciousness and risk aversion, as well as rational factors, consisting of the level of knowledge, price consciousness and involvement in the purchase intentions.

Due to the necessity of finding the most crucial industry-specific indicators, it was decided to carry out two-stage research based on qualitative preliminary research and the main study. The findings obtained from the qualitative studies were integrated with insights gleaned from the literature to finalize the study’s conceptual model. The main study was based on a descriptive research design, and data were collected through an online survey. Structural equation modeling was the main technique used to test the model.

Health consciousness, level of knowledge and involvement in consumer purchase intentions affect either attitude or perceived behavioral control. Moreover, attitudes toward OTCs and perceived behavioral control were significant predictors of purchase intentions for OTC drugs. At the same time, subjective norms did not have a significant effect.

Knowing what influences consumer purchase intentions contributes to developing more effective marketing strategies and actions.

This study took an integrative approach to understanding the effect of personal and rational factors within the TPB framework for a changing tendency in consumer behavior of specific product categories.

The primary objective of this paper is to explore how the companies in Turkey plan, manage, carry out and improve their logistics processes and, thus, provide a preliminary analysis to explore the current status of logistics in Turkey. For this purpose, a structured‐disguised survey was conducted with the top 250 firms of Istanbul Chamber of Commerce. The aims were: to specify the organizational, financial and managerial significance of logistics activities; to articulate the sourcing/ purchasing feature, customer service and order processing, to understand the changes in the number of suppliers and customers and to identify the features of activities and tools aimed to improve the quality/productivity in these systems; and to investigate the impact of the general characteristics on the first and second subcriteria for each firm.

The purpose of this paper is to empirically explore how prior exchange relationships may impact a local firm's decision to participate in a supply chain international joint venture (SC‐IJV).

An exploratory study of the influence of prior working relationships using a sample of 32 US and European‐based SC‐IJVs. First, a cluster method was used to determine the two categories of local firms' motivation to form SC‐IJVs with foreign partners. Next, a discriminant analysis revealed the differences between the two categories in terms of the emphasis put on prior exchange relationships when forming SC‐IJVs.

Results confirm two major types of motivations for a local firm to participate in a SC‐IJV: exploration and exploitation. Moreover, the motivation types are varied in terms of the emphasis placed on a prior working relationship with the SC‐IJV partner. While exploitation‐oriented firms consulted a previous working relationship experience before forming a supply chain IJV, exploration‐oriented firms did not.

This study shows that Eastern European firms can be separated into two categories in terms of SC‐IJV goals: those focused on learning (exploration) and those focused on strategic behavior (exploitation). Second, the study shows that the firms favoring exploitation prefer a past working relationship with the potential partner, while those favoring exploration did not. The study is exploratory in nature, but provides a solid platform on which to base future research.

The aim of this study is to develop an applicable and detailed model for customer lifetime value (CLV) and to highlight the most important indicators relevant for a specific industry – namely the banking sector.

This study compares the results of the least square estimation (LSE) and artificial neural network (ANN) in order to select the best performing forecasting tool to predict the potential CLV. The performances of the models are compared by the hit ratio, which is calculated by grouping the customers as “top 20 per cent” and “bottom 80 per cent” profitable.

Due to its higher performance; LSE based linear regression model is selected. The results are found to be highly competitive compared with the previous studies. This study shows that, beside the indicators mostly used in the literature in measuring CLV, two additional groups, namely monetary value and risk of certain bank services, as well as product/service ownership-related indicators, are also significant factors.

Organisations in the banking sector have to persuade their customers to use certain routine risk-bearing transaction-based services. In addition, the product development strategy has a crucial role to increase the CLV of customers because some of the product-related variables directly increase the value of customers.

The proposed model predicts potential value of current customers rather than measuring current value considered in the majority of previous studies. It eliminates the limitations and drawbacks of the majority of models in the literature through simple and industry-specific method which is based on easily measurable and objective indicators.