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The purpose of this study is to examine the impact of New Public Management (NPM) reforms/practices on innovation in public sector organizations. Although much is written on NPM, the assumption that NPM reforms stimulate organizational innovation has not been empirically tested. The present study is an attempt to bridge this research gap.

Building on open-systems approach, institutional theory, and innovation research, this study argues that organizational innovation occurs in response to stimuli in the external (environmental factors) and internal environment (organizational factors), considering NPM reforms/practices as a proxy for external or environmental factors. Organizational factors include formal structural complexity, senior management support and job security. The study tests this model using data from a national survey conducted in five states in the US.

The study provides empirical insights into our understanding of the factors that drive innovation in public sector organizations. The study finds that although environmental factors are as significant as organizational factors in driving innovation in the public sector, senior management support remains the most important predictor of innovation.

This paper fulfils an identified need to study the effect of NPM reforms and practices on innovation in public organizational settings.

This chapter presents a summary of the literature on the influence of the Internet and other digital innovations on markets, consumers, and firms. The review leads to a list of topics in need of research in the general areas of big data, online and mobile advertising, consumer search, online privacy, online reviews, social networks, platforms for online transactions, and the impact of the Internet on retail markets, including multi-channel and omni-channel retailing. We discuss the big data approaches that have been applied to problems of targeting and positioning and suggest areas for further development of these approaches. We also discuss the emerging area of mobile advertising, which can further enhance targeting. On the consumer side, the evidence indicates that the Internet has greatly lowered the costs of search and access to retailers. Much of the consumer data are transmitted to sellers, and much of the online advertising is transmitted to consumers, through platforms, such as Google. We conclude that better models of competition among these platform firms are needed and that they need to be examined for anti-trust violations. While online retailing has grown rapidly, it still has a relatively small share of retail sales. Since sellers can combine the advantages of online and offline channels, it has been common for sellers to branch into multi-channel retailing. Given the increased availability of detailed consumer data, omni-channel selling, which emphasizes strategies for the various touchpoints that lead to a transaction, is an area for further development.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

The purpose of this paper is to establish criteria for evaluating strategic partners in a network of logistics service providers (LSPs) to show how analytical network process (ANP) can be used to identify the weights of these criteria on a case-specific basis, and to investigate whether the ANP model can be used as a starting point to evaluate strategic partners for other LSP networks.

Based on a literature review of vertical cooperation, the authors develop an overview of criteria for the evaluation of partners in a network of LSPs. The authors then apply ANP at LSP1 to validate the criteria, identify weights for these criteria and to validate model outcomes. Furthermore, the authors investigate whether the ANP model developed for LSP1 can be applied to another LSP with similar characteristics (LSP2). In-depth interviews are used to draw conclusions on the modeling approach and the model outcomes.

The research shows that evaluation criteria for partners in vertical partnerships between shippers and LSPs are applicable to LSP partners in horizontal partnership networks. The ANP model with criteria weights provides a good starting point for LSPs to customize the evaluation framework according to their specific needs or operating environments.

Limited research is available on evaluating LSP partners in horizontal partnerships. To the best of the authors’ knowledge, this paper is the first to bring forward horizontal LSP partner evaluation criteria to develop an ANP model for LSP partner evaluation and to apply this to two cases, and to provide a starting point for evaluating partners in similar horizontal LSP networks.

The purpose of this paper is to investigate how to manage business relationships in retail operations. In particular, this study examines the links between business relationships with principal stakeholders, operations strategy, and performance.

Drawing upon the stakeholder theory, this study employs a path analytic framework to understand the effects of business relationships on retail operations strategy and performance for a sample of retail companies in China.

The findings indicate that good relationships with customer and supplier could enable retailers to be more efficient in developing quality and flexible strategies, and that low cost and flexible strategies, in turn, influence business performance. However, the results suggest that the vital role that the Chinese government played in economic activities in the 1980s and 1990s has diminished gradually. It is appropriate to treat the good relationship with government bodies as a necessary, but not sufficient, condition for retail success in China.

This study seems to be the first in investigating the effects of business relationships on operations strategy and performance applying a stakeholder theory perspective.

The purpose of this paper is to help supply chain (SC) decision makers successfully penetrate through SC collaboration and strengthen their SC in the global market by understanding collaborative activities, and understand how these activities are related to each other in the SC.

This paper develops a set of collaborative activities from literature, and the developed model is helpful for SC decision makers to monitor their SC activities and take corrective actions to improve collaboration in their SC by using interpretive structural modeling (ISM) and MICMAC analysis.

This study reveals that collaborative activities increase the value of whole SC. The various activities are modeled on the basis of “an activity influencing other activities” and “an activity influenced by other activities,” which is useful for SC managers to take a decision.

The current study is literature based; therefore, there would be need of more explanation of the activities which lead to understand and implement SC collaboration in case of service and manufacturing industry.

The model of this study is helpful for decision makers to implement supply chain collaboration (SCC) and to understand various SCC activities on the basis of their driving and dependence power.

This research provided insight into skills needed for SC decision makers to implement collaboration in the SC using ISM. The results of the study could be adopted to monitor the existing SCC program or design new collaboration program to meet the global market requirements. To the best of knowledge, there is no reference that discusses SC collaborative activities on the basis of their driving and dependence powers.

The purpose of this paper is to develop a new composite model using structural equation modelling (SEM) and analytic hierarchy process (AHP) for the selection of suppliers.

In this paper the authors have made an attempt to arrive at the supplier selection score using SEM and AHP. An attempt has been made to develop a new composite model using SEM and AHP technique, based on the survey of 151 respondents. Attributes' weightage are found out using cluster analysis.

Based on the output from the composite model, cluster analysis has been carried out to find out the strengths and weakness of each supplier on the influencing factors. Based on these findings, the supplier can improve on factors where they lag and can maintain the factors where they excel.

In this paper the authors have made an attempt to arrive at the supplier selection score using SEM and AHP.

The objective of this chapter is to interpret a supply chain as an ontological entity with being-in-the-world of spacetimemattering. A case study approach is adopted to reveal the strategies undertaken by one of China’s fastest growing Internet companies – Xiaomi Inc. – to create competitive advantage through its management of product design and supply chain integration. Utilizing publicly available data, we analyze the company with quantum storytelling and network analysis techniques. Our analysis concludes that Xiaomi’s success originates from two aspects. First, Xiaomi is a good storyteller, who makes stories appealing to customers by involving them into product design and branding. Second, Xiaomi’s parsimonious supply chain substantially improves its market responsiveness and reduces disruption risks; more importantly, it helps to offer products of great value to customers.

The purpose of this paper is to propose an innovative buyer-supplier performance measurement system (PMS) (called relationship regulator – RelReg), aimed at stimulating collaboration on mutual performance. The RelReg is described all throughout the phases of its lifecycle: first, design features and visual representation of the new measurement framework are reported; second, guidelines on how to implement, use and review the system are provided, highlighting the role of the buyer and the supplier at each step.

A theory building and testing approach is applied. The RelReg developed features primarily ground on previous scientific contributions matched with empirical evidence collected through case studies, workshops and focus groups. The resulting conceptual model is then validated through a dyadic buyer-supplier case study.

Two conceptual frameworks are provided: the RelReg dashboard – a multidimensional PMS; and the RelReg lifecycle – set of activities to be performed by both the buyer and the supplier all along the adoption process. Moreover, empirical insights on relevant issues to be considered when adopting the RelReg are reported.

The RelReg represents an innovative and smart tool, allowing buyer-supplier dyads to collaborate on relationship performance.

The purpose of this paper is to test the impact of power (coercive and non-coercive) on supply chain (SC) relationship success. In particular, this paper aims to examine the moderating effect of relationship quality (RQ) on the associations between power bases and relationship success. In turn, RQ and relationship success can impact the operational performance of the suppliers.

A survey of 284 Australian suppliers (growers of organic fruits and vegetables) was conducted to elucidate the associations among power, RQ, relationship success and operational performance.

RQ was found to only moderate the positive relationship between non-coercive power and relationship success. Moreover, RQ and relationship success were positively related to the suppliers’ operational performance. The findings conform those of previous research in which coercive power is negatively related to the supplier’s perception of relationship success while non-coercive power is positively related to the supplier’s perception of relationship success. Relationship success was found to mediate the relationships between the power bases and operational performance.

This study only collected data from one-side of the dyad (the suppliers). Another potential limitation is the existence of respondent bias, which can arise when a single respondent is asked to assess both the nature of the relationship and the performance outcomes of the relationship.

This study highlights the importance for managers to understand the dynamic shaping relationships when they want to signal their influence and RQ in inter-firm relationships associated with the SCs.

This study applies signaling theory as an alternative theoretical lens that looks into the moderating role of RQ on the associations between power bases and relationship success in the SC. This study argues that when the buyer increasingly develops RQ, the effect of non-coercive power on relationship success diminishes. Both RQ and relationship success positively influence the supplier’s operational performance.