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This study aims to explore the dynamic interplay of key resources (i.e. research and development (R&D), advertising and exports) in affecting the performance of USA manufacturing firms. Specifically, the authors examine the dynamic impact of joint resources and predict differential effect scales contingent on firm capabilities.

This study presents a combined multiple regression analysis (MRA)-multilayer perceptron (MLP) neural network modeling and investigates the complex interlinkage of capabilities, resources and performance. As an innovative approach, the MRA-MLP model investigates the effect of capabilities under the combinatory deployment of joint resources.

This study finds that the impact of joint resources and synergistic rents is not uniform but rather distinctive according to the combinatory conditions and that the pattern is further shaped by firm capabilities. Accordingly, besides signifying the contingent aspect of capabilities across a range of resource combinations, the result also shows that managerial sophistication in adaptive resource control is more than a managerial ethos.

The proposed analytic process provides scientific decision support tools with control mechanisms with respect to deploying multiple resources and setting actionable goals, thereby presenting pragmatic benchmarking options to industry managers.

Using the theoretical underpinnings of the resource-based view (RBV) and resource orchestration, this study advances knowledge about the complex interaction of key resources by presenting a salient analytic process. The empirical design, which portrays holistic interaction patterns, adds to the uniqueness of this study of the complex interlinkages between capabilities, resources and shareholder value.

This paper explores the nonlinear interactions of research and development (R&D) and advertising and their synergistic effect on firm performance using Tobin's Q. This study also aims to investigate differential synergy patterns under varying levels of exports with a precision impact on performance.

Unlike a conventional statistical approach, this study uniquely presents a neural network approach to explore the dynamic interplay of strategic factors. A multilayer perceptron neural network (MPNN) is designed to capture complex interaction patterns through a predictive analytic process.

This study finds that the impact of R&D and advertising is positive, with a greater effect on high-export firms. Moreover, the experiment results show that the synergy of R&D and advertising goes beyond the formatted positive/negative frame and actually has a reinforcing effect.

This study not only conveys the significant nexus of R&D and advertising for firm performance but also provides industry managers' practical means to assess the joint effect of R&D and advertising on firm performance. The proposed analytic mechanism in particular provides pragmatic decision support to managers in harmonizing their R&D and advertising efforts for a foreseeable impact.

This paper presents an innovative analytic process using the MPNN to explore the synergy between R&D and advertising. In addition to offering new perspectives on R&D and advertising, this study presents pragmatic implications for managing those strategic resources to meet performance targets.

This study aims to explore the strategic impact of R&D and export activity on the diverse dimensions of US manufacturing firms’ performance. It also explores, using a predictive analytic model, the interactive synergistic effect that R&D and exports have on firm performance.

This study presents an innovative two-stage regression-neural network approach. Complementing conventional statistical analysis, the predictive backpropagation neural network explores the relative impact of R&D and exports and their synergistic effect on firm performance.

This study demonstrates the significant and positive effect of R&D and export strategy/activity on the economic performance of leading US manufacturing firms, particularly on their market-based performance (i.e. sustained growth rate or SGR). Furthermore, this study finds that the synergistic effect of R&D and exports on short-term performance (i.e. return on investment) is positive in high-tech firms but negative in low-tech firms. However, the synergistic effect on SGR is increasingly positive regardless of the level of technology.

In addition to traditional statistical analysis, this study uniquely investigates the relative importance of selected strategic variables, along with R&D and export activity and their differential synergistic effects, for firms’ economic performance in contrasting industry settings (high-tech vs low-tech).

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.

The purpose of this paper is to develop an artificial neural network (ANN) based prediction model via integration with data envelopment analysis (DEA) to provide the means of predicting incremental performance goals. The findings confirm the usefulness of the herein developed prediction approach, based on the results of analyses of time series data from the smartphone industry.

A two-stage hybrid model was developed, incorporating sequential measurement and prediction capability. In the first stage, a Chames, Cooper, and Rhodes DEA model is the preprocessor, generating efficiency scores (ES) of decision-making units (DMUs). In the second or follow-on stage, the ANN prediction module utilizes knowledge variables and ES to predict the change in performance needed for a desired level of improvement.

This combined approach effectively captured the information contained in the industry’s turbulent characteristics, and subsequently demonstrated an adaptive prediction capability. The back propagating neural network successfully predicted the incremental performance targets of DMUs, which translated the desired improvement levels into actionable performance goals, e.g., revenue and operating income.

This paper presents an incremental prediction approach that supports better practice benchmarking. This study differentiates itself from previous research by introducing an adaptive prediction method which generates relevant quantity outputs based upon desired improvement levels. The proposed modeling approach integrates performance measurement with a prediction framework and advances benchmarking practices to enable better performance prediction.

The purpose of this paper is to present a research model that defines the inter‐relationships between strategic green orientation, integrated product development, supply chain coordination, green performance outcomes and business unit performance. This paper aims to address innovation issues by integrating strategic orientation, internal business practices, supply chain coordination, and performance outcomes measures.

The international data of 711 firms accessed through the International Manufacturing Strategy Survey (IMSS IV) are used to validate this model.

A firm's strategic green orientation involves past green practices, implementation of innovative environment improvement program and future commitment for environmental practices. This strategic green orientation is supported by a set of inter‐organizational innovation practices such as integrated product development practices, effective coordination of supply chain network and relevant and measurable performance outcomes.

The model, variables, empirical tests and results in this paper suggest a new understanding about strategic green orientation and its relationships with product development practices and supply chain coordination. The framework is intended both to explicitly inform senior executives of the importance of inter‐organizational innovation practices such as strategic green orientation in terms of past, present and future practices as well as to the factors that effectively implement such strategic direction and commitment. It is also intended to provide a lens with which further research can be directed to enhance environmental reputation and outcomes of firms through new product development practices and supply chain network coordination and the sustainable long‐term competitive advantages of the firms.

The purpose of this study is to benchmark the wireless mobile communication service providers in the USA for the relative efficiencies of assets and expenses in conjunction with revenues. In addition, the impact of merger activities on the efficiencies will be investigated.

The authors use data envelopment analysis (DEA) to measure comparative efficiencies of wireless mobile communication companies. Data include annual reports showing assets, expenses, and revenues.

For the relative efficiencies of total asset utilization, eight decision‐making units (DMU) out of 16 are 100 percent efficient. Likewise, seven DMU's are 100 percent efficient in the current asset model. However, only five DMU's are 100 percent efficient in the expense model. Accordingly, the companies maintain relatively higher efficiencies for asset management than those for expense management. Merger activities adversely affect the efficiencies of the companies in the models. Thus, the companies need to make stronger efforts to improve their efficiencies after consolidation.

This study is subject to the limitations of financial data and DEA that measures relative technical efficiencies of DMU. Results will vary according to data and DMU included in the model.

The major contributions of this study are that this is the first attempt of benchmarking using DEA in the wireless telecommunication industry in the USA and its investigation of the impact of merger and acquisition activities on efficiencies.

The purpose of this paper is to explore and describe a sustainable business model in a service industry.

A case study was performed during 2012-2013. It is based upon a major Scandinavian hotel chain known for having implemented documented, extensive and systematic sustainable business practices within the company and in its business network. Data were gathered from multiple sources to explore and describe their sustainable business model.

This study provides a validation in a service industry of an assessed sustainable business model derived from a goods industry and from other industries as well. The empirical findings indicate that the model appears to be universally applicable across sources and stakeholders in the service sector beyond company- and industry-specific characteristics in services.

Further research that may validate or falsify current empirical findings in other business settings is presented. Suggestions for further research are provided, such as a focus on similarities and differences across companies, industries and countries worldwide.

Environmental initiatives and efforts need to go hand-in-hand with the social and economic ones. The interconnection between environmental, social and economic elements is necessary and crucial if it is to be successful in the marketplace and society.

A sustainable business model is not about simplistic initiatives and efforts of sustainable business practices. A multitude of initiatives and efforts are required in the marketplace and society.

It contributes to visualize an all-embracing perspective on the challenges, complexities and dynamics of implementing sustainable business practices within and beyond corporate or organizational boundaries toward business networks in the marketplace and society.