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This research aims to determine the current situation of outsourcing logistics activities in Turkey, a country which has a great potential for logistics activities among the surrounding continents because of its geographical location.

Survey analysis was conducted with 250 of the top 500 Turkish firms specified by the Istanbul Chamber of Commerce for the year 2001. These firms are selected based on the firms' total sales volume, profitability, and other financial performance indicators. The responses were evaluated statistically using SPSS software. Factor and cluster analyses were utilized to determine the behavior of respondents against their logistics service providers and to categorize the respondents.

The focus of the part of the survey is basically in the following areas: logistics functions outsourced by Turkish firms; the extent to which logistics functions in general and transportation functions in particular are outsourced by Turkish firms; and the criteria used to select and evaluate the performance of the outsourcing firms. The performance of the first three outsourcing firms currently used by Turkish firms.

This study shows that the power of third party logistics (3PL) firms in Turkey is underestimated. The firms that outsource their logistics activities in Turkey are 95 percent foreign capitalized. Turkish businessmen think that they should do their business themselves and they are not aware of the benefits of outsourcing logistics activities. In fact, in selecting the transportation carrier, they consider different criteria but the general tendency is either to select the carrier that has a good reputation and/or the one which is easy to collaborate with.

This paper aims to examine the direct impacts of marketing resources and marketing activities on several business performance indicators in the banking industry and the indirect effects through customer-based brand equity.

We use a holistic empirical approach based on resource-based view and marketing productivity chain. The main study consists of a secondary analysis using quarterly data of fourteen banks over four years. We analyze the data using fixed-effect panel data regression, namely seemingly unrelated regressions.

We find that customer-based brand equity is one of the most influential factors on business performance. Moreover, the indirect effect through customer-based brand equity should be considered in improving business performance. Marketing-related financial resources positively impact customer-based brand equity and business performance. Regarding marketing activities, pricing strategies affect the bank preferences of customers, which in turn affect the growth of deposit volumes and churn rates. Additionally, the number of bank branches positively impacts business performance. Advertising spending on different media has differentiated impacts on the performance indicators; thus, the allocation of advertising budget and advertising planning are critical.

This study examines the inter-relationships among marketing resources, marketing activities, consumer response through brand equity and marketing performance. This study contributes to the literature by integrating the resource-based view and the marketing productivity chain to analyze the inter-relationships using panel data and several sector-related metrics. This study provides valuable insights to decision-makers in the banking industry.

This research compares the logistics systems of Asia and Europe and categorises them into distinct levels of logistics excellence. First, the context in Asia and in Europe is summarized. Then, attributes of a world‐class logistics system are proposed. By applying cluster analysis to data from authoritative sources, we objectively segregate European and Asian logistics systems into three logistics tiers. There are several surprises, the main one being that the UK is classified Tier 2 (not as favourable as Tier 1). A prioritized set of attributes that the UK could improve on to qualify for the Tier 1 group is suggested. Sensitivity analyses are conducted to determine changes to the classifications. After finding that the top‐ranking logistics systems of Europe and Asia are from Denmark and Singapore, respectively, those two countries are studied in detail to draw logistics lessons applicable elsewhere.

This study aims to measure the quality of life (QoL), using the proposed conceptual model within the context of the interactions between human behaviour and the environment. It aims to research the effect of the control of residents on QoL, and the findings constitute the basis for a discussion on the effects of environmental policies on QoL. Doğanbey in Bursa, which is in close proximity to the Khans (Inns) District, is selected as the study area because it represents an urban transformation implemented according to administrative decisions. QoL measurements in the research reflect people's evaluations, their adaptation to post-transformation environment and the implementation process by means of questionnaires and in-depth interviews. The findings indicate that lack of resident control has an adverse effect on satisfaction and QoL, and that an urban transformation conducted without user participation will fail. In conclusion, the study aims to contribute to decision-making mechanisms within the framework of residents' influence to ensure increased QoL. The intention of the authors is to promote the usage of QoL scale developed for other housing or transformation areas by modifying the questions according to varying geographies and social groups.

Logistics service providers (LSPs) may invest a lot of time in tenders unsuccessfully, as they do not meet the expectations of logistics service users (LSUs). In order to help them classify and target their customers more efficiently and effectively and make logistics outsourcing more successful for both LSUs and LSPs, this paper analyzes underlying dimensions of criteria German manufacturing and trading companies actually use in selecting an LSP and clusters of LSUs based on these dimensions.

A questionnaire survey with 110 manufacturing and 135 trading companies was conducted in Germany. Principal component analysis (PCA), cluster analysis, multivariate analysis of variance, analysis of variance and discriminant analysis were performed on the sample data.

PCA revealed eight dimensions of LSU criteria in selecting LSPs and that cost alone seems not decisive. Based on these dimensions, cluster analysis produced nine LSU groups. These groups differ the most in the selection criteria dimensions cost-performance ratio, operational collaboration, quality and locations. Recommendations for servicing these groups are given. The two largest groups, which make up 43.5%, seem not that demanding and price sensitive. The selection criteria dimensions and LSU groups enable LSPs to classify and target their customers more efficiently and effectively, to evaluate and develop their core competencies, and contribute to successful logistics-outsourcing relationships.

This research is the first to examine selection criteria dimensions and resulting clusters of German manufacturing and trading companies in order to make logistics outsourcing more successful.

The purpose of this paper is to identify the causal relationship between the independent variables such as process quality and product innovation in regard to the dependent variable of competitiveness, in the manufacturing sector of the automotive industry, specifically for Tier 1 auto parts manufacturing companies in the state of Nuevo León, México.

Based on a thorough review of the literature, an instrument was designed to measure the competitiveness of automotive industry manufacturing; it was applied to Tier 1 suppliers of automotive parts in the state of Nuevo León, México. Various statistical analysis tests were applied to the results; first, Cronbach's alpha to determine the reliability of the instrument, and next, a factor analysis to measure construct validity. In addition, a multiple linear regression analysis was carried out to identify the causal relationships between the variables analysed.

This study found that process quality as well as product innovation have independent and positive causal relationships with respect to competitiveness in automotive manufacturing.

The limitations of this paper are its focus on a single manufacturing sector, application in a single country and small sample size.

Companies that supply automotive parts will be able to invest in the key elements of quality and innovation while focusing their efforts on reinforcing their competitiveness.

Auto parts suppliers will be able to perform specific actions to improve their quality processes through lean manufacturing and quality assurance practices and by designing new and innovative products that will enhance their competitiveness.

This work examines the relationship between transformational leadership and enablers and results of the European Foundation for Quality Management (EFQM) model 2013 and the relationship between the EFQM model elements (enablers and results).

The work employs partial least squares (PLS) and empirical data from 102 hotels.

Transformational leadership is key to facilitate the enablers of the EFQM model (strategy, people, partnership and processes) and improve customer, employee and social results and organizational performance.

The present work expands previous studies that focus on analyzing the link between enablers and results of EFQM by showing that transformational leadership is an appropriate leadership style to foster the EFQM model elements. It empirically supports the view that transformational leadership is a facilitator that can foster the EFQM enablers and improve results in hotels. Thus, it also sheds light on the controversial link between transformational leadership and various performance measures (customer, employee and social results and organizational performance).

In this paper, we propose a scenario based global supply chain planning (GSCP) process considering demand uncertainty originated from various global supply chain risks. To generate the global supply chain plan, we first formulate a GSCP model. Then, we need to generate several scenarios which can represent various demand uncertainties. Lastly, a planning procedure for considering those defined scenarios is applied. Unlike the past related researches, we adopt the fuzzy set theory to represent the demand scenarios. Also, a scenario voting process is added to calculate a probability (possibility) of each scenario. An illustrative example based on a real world case is presented to show the feasibility of the proposed planning process.

The purpose of this paper is to investigate the feasibility of using artificial neural networks (ANNs) in conjunction with data envelopment analysis (DEA) for the performance measurement of major mobile phone providers, and for subsequent predictions related to best performance benchmarking and decision making.

DEA and ANN are combined, providing an integrated modeling approach via a two-stage process. DEA is used for front end measurement, while ANN provides learning and prediction capabilities. DEA analysis of industry characteristics is based on the measurement of each decision-making unit's (DMU) performance. Back propagation neural networks (BPNN) can then predict each DMU's efficiency score, based on the results of the DEA models. Additional BPNN models provide best performance predictions.

The DEA module successfully evaluates the competitive status of firms in the mobile phone industry in terms of efficiency. Efficiency trends over the observation period reveal the dynamic nature of competition in this industry. The predictive power of the BPNN module has been demonstrated as well. The proposed system is an effective benchmarking and decision support tool, via its capability to simulate performance scenarios, thereby facilitating insightful, prudent decision making.

This paper proposes the use of two different but complementary methods, DEA and ANN, in a combined performance modeling approach, and examines mobile phone providers. This methodology can improve users’ performance benchmarking and decision-making processes. Additionally, adaptive prediction capability is provided through approximating efficient frontiers, in addition to performance measurement.

The purpose of this paper is to design an innovative performance modeling system by jointly using data envelopment analysis (DEA) and artificial neural network (ANN). The hybrid DEA-ANN model integrates performance measurement and prediction frameworks and serves as an adaptive decision support tool in pursuit of best performance benchmarking and stepwise improvement.

Advantages of combining DEA and ANN methods into an optimal performance prediction model are explored. DEA is used as a preprocessor to measure relative performance of decision-making units (DMUs) and to generate test inputs for subsequent ANN prediction modules. For this sequential process, Charnes, Cooper, and Rhodes and Banker, Chames and Cooper DEA models and back propagation neural network (BPNN) are used. The proposed methodology is empirically supported using longitudinal data of Japanese electronics manufacturing firms.

The combined modeling approach proves effective through sequential processes by streamlining DEA analysis and BPNN predictions. The DEA model captures notable characteristics and efficiency trends of the Japanese electronics manufacturing industry and extends its utility as a preprocessor to neural network prediction modules. BPNN, in conjunction with DEA, demonstrates promising estimation capability in predicting efficiency scores and best performance benchmarks for DMUs under evaluation.

Integration of adaptive prediction capacity into the measurement model is a practical necessity in the benchmarking arena. The proposed framework has the potential to recalibrate benchmarks for firms through longitudinal data analysis.

This research paper proposes an innovative approach of performance measurement and prediction in line with superiority-driven best performance modeling. Adaptive prediction capabilities embedded in the proposed model enhances managerial flexibilities in setting performance goals and monitoring progress during pursuit of improvement initiatives. This paper fills the research void through methodological breakthrough and the resulting model can serve as an adaptive decision support system.