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Climate change requires the reduction of direct and indirect greenhouse gas (GHG) emissions, a task that seems to clash with increasing supply chain complexity. This study aims to analyse the upstream supply chain complexity dimensions suggesting the importance of understanding the information processing that these may entail. Reducing equivocality can be an issue in some dimensions, requiring the introduction of written guidelines to moderate the effects of supply chain complexity dimensions on GHG emissions at the firm and supply chain level.

A three-year panel data was built with information obtained from Bloomberg, Trucost and Compustat. Hypotheses were tested using random effect regressions with robust standard errors on a sample of 394 SP500 companies, addressing endogeneity through the control function approach.

Horizontal complexity reduces GHG emissions at the firm level, whereas vertical and spatial complexity dimensions increase GHG emissions at the firm and supply chain level. Although the introduction of written guidelines neutralises the negative effects of vertical complexity on firm and supply chain GHG emissions, it is not sufficient in the presence of spatial complexity.

This paper offers novel insights by suggesting that managers need to reconcile the potential trade-off effects on GHG emissions that horizontally complex supply chain structures can present. Their priority in vertically and spatially complex supply chain structures should be to reduce equivocality.

Numerous scales have been developed and utilized in the tourism and hospitality field, yet, their psychometric properties have not been systematically reviewed and evaluated. This gap compromises researchers' ability to develop better measures and improve measurement decisions. In this current study, 56 scales were identified and evaluated in terms of their psychometric properties. It was found that most scales were imperfect in measuring tourism and hospitality domains, and most scales did not provide explicit information about the scale development procedures that were adopted. The scale development procedure and psychometric properties of the reviewed scales are summarized, evaluated, and recommendations are made for future tourism and hospitality scale development.

This paper aims to test the finite sample properties of the automatic variance ratio (AVR) test and suggest suitable measure to improve its small sample properties under conditional heteroskedasticity and apply it to test the martingale hypothesis in the stock prices of the Portugal, Ireland, Italy, Greece and Spain (PIIGS economies) markets. This paper also seeks to investigate that “If the time series is not martingale, then what else?”

Monte Carlo experiments have been undertaken to test the small sample properties of automatic variance ratio (AVR) test. The study uses AVR test on daily and weekly data of the indices to investigate their martingale behaviour. It uses detrended fluctuation analysis (DFA) and BDS test statistics to answer, “If not martingale, then what else?”. The study also applies moving subsample approach to examine the dynamic behavior of stock prices and to obtain inferential findings robust to possible structural changes and presence of influential outliers.

The author finds that weighted bootstrap procedure significantly improves the small sample properties of AVR tests under conditional heteroskedasticity. The results provide evidence in support of the weak‐form efficiency of Italy and Spain. But Portugal, Ireland and Greece exhibit signs of long memory in the stock prices. All indices also exhibit chaotic characteristics.

This paper has both methodological and empirical originality. On the methodological aspect, the author proposes weighted bootstrap procedure on AVR test to improve its small sample properties. On the empirical side, the study finds that all stocks exhibit dynamic behavioral characteristics which change over time.

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

Encouraged by a variety of stakeholders and the benefits that could derive from corporate social responsibility (CSR), multinational corporations (MNCs) are increasingly concerned with limiting the social and environmental costs of their operations. Yet, they are often accused of not walking the talk on sustainability. Since offshoring and outsourcing became mainstream in international business, concerns have particularly emerged around MNCs’ ability to implement credible and efficient sustainability strategies along increasingly complex and dispersed global supply chains. Evidence on the effectiveness of private initiatives to socially and environmentally upgrade supplier practices remains mixed, and the different insights from the literature, siloed. This work aims to provide a survey of the literature documenting the challenges MNCs face trying to implement more sustainable policies in supplier networks located in developing countries, where sustainability standards and certifications are likely to be poorly enforced. We integrate insights from multiple disciplines, provide an overview of the existing body of research on the topic, and propose an analysis structured around three recurring themes: the policy tools available to MNCs and their limitations, the obstacles to suppliers’ compliance, and the different governance mechanisms available to MNCs to shape their suppliers’ practices. By providing a comprehensive picture of CSR policy implementation challenges, we contribute to practice and highlight potential avenues for future research.

Based on the emerging view of supply chains as complex adaptive systems, this paper aims to build and test an analytical model for resilience assessment surrounding supply chain risks at the level of the supply chain system and its individual tiers.

To address the purpose of this study, a multimethod research approach is adopted as follows: first, data envelopment analysis (DEA) modelling and fuzzy set theory are used to build a fuzzy network DEA model to assess risk resilience of the overall supply chains and their individual tiers; next, the proposed model is tested using a survey of 150 middle- and top-level managers representing nine industry sectors in Iran.

The survey results show a substantial variation in resilience ratings between the overall supply chains characterizing nine industry sectors in Iran and their individual tiers (upstream, downstream and organizational processes). The findings indicate that the system-wide characteristic of resilience of the overall supply chain is not necessarily indicative of the resilience of its individual tiers.

High efficiency scores of a number of tiers forming a supply chain are shown to have only a limited effect on the overall efficiency score of the resulting supply chain. Overall, our research findings confirm the necessity of adopting both the system-wide and tier-specific approach by analysts and decision makers when assessing supply chain resilience. Integrated as part of risk response and mitigation process, the information obtained through such analytical approach ensures timely identification and mitigation of major sources of risk in the supply chains.

Supply chain resilience assessment models rarely consider resilience to risks at the level of individual supply chain tiers, focusing instead on the system-wide characteristics of supply chain resilience. The proposed analytical model allows for the assessment of supply chain resilience among individual tiers for a wide range of supply chain risks categorized as upstream, downstream, organizational, network and external.

The purpose of this paper is to investigate the major factors influencing retailer’s optimal ordering strategy in a supply chain consisting of one supplier and one retailer, where the retailer is newsvendor-like and capital-constrained, and further explore the issue of supply chain coordination.

Based on bi-objective programming which is modeled under the mean-variance framework, the retailer’s optimal ordering strategy is derived. Furthermore, through comparative analysis between decentralized system and centralized system along with a numerical simulation, this study examines the theoretical conclusions about supply chain coordination.

This study shows that a poor retailer with a high Expected Terminal Wealth Target Threshold (ETWTT) would ignore bankruptcy risk and order more, whereas a rich retailer is relatively conservative. It also reveals that in some cases, the optimal order quantity and performance of decentralized system could be both improved. However, the centralized system can always get more profit than the decentralized one.

This study uses a bankruptcy threshold to describe retailer’s bankruptcy risk, and considers retailer’s wealth status to formulate the model as an innovative bi-objective programming. The type of retailer as rich or poor in terms of his wealth status and asset structure is distinguished. Moreover, the impacts of retailer’s type and ETWTT on ordering strategy are examined.