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This paper examines the effects of virtual advertising in a sports broadcast setting. We analyse the conspicuousness of virtual advertising and match the results with explanatory variables like brand awareness, duration of exposure and frequency of exposure. Furthermore, we measure the role of attitudes towards advertising in general and its impact on attitudes towards virtual advertising of the respondents. Our results indicate that most respondents recognise virtual advertising as such. Advertising effectiveness is driven to a large degree by the frequency of exposure. A positive attitude towards advertising in general leads to a positive attitude towards virtual advertising of the participants.

The purpose of this study is to map current knowledge on big data analytics (BDA) for supply chain risk management (SCRM) while providing future research needs.

The research team systematically reviewed 53 articles published between 2015 and 2021 and further contrasted the synthesis of these articles with four in-depth interviews with BDA startups that provider solutions for SCRM.

The analysis is framed in three perspectives. First, supply chain visibility – i.e. the number of tiers in the solutions; second, BDA analytical approach – descriptive, prescriptive or predictive approaches; third, the SCRM processes from risk monitoring to risk optimization. The study underlines that the forefront of innovation lies in multi-tiered, multi-directional solutions based on prescriptive BDA to support risk response and optimization (SCRM). In addition, we show that research on these innovations is scant, thus offering an important avenue for future studies.

This study makes relevant contributions to the field. We offer a theoretical framework that highlights the key relationships between supply chain visibility, BDA approaches and SCRM processes. Despite being at forefront of the innovation frontier, startups are still an under-explored agent. In times of major disruptions such as COVID-19 and the emergence of a plethora of new technologies that reshape businesses dynamically, future studies should map the key role of such actors to the advancement of SCRM.

This study examines a way to enhance the effectiveness of commercials embedded in the telecasts of mega sporting events. We hypothesise that embedded commercials are more likeable when the cues of the commercials match the motivations induced by the telecasts of sports games. Specifically, we posit that the telecasts of mega-sporting events induce motives of relatedness (rather than motives of competence) and that the advertisements embedded in these telecasts will be more effective when they appeal to the relatedness motive. The results support this hypothesis. Theoretical and practical implications are discussed and future research directions are provided.

The purpose of this paper is to investigate the supply chains for organic milk, apples and pasta in eight European countries and how added value is distributed among market players.

Using secondary data and expert interviews, a market overview for the three products is provided as the basis of a more detailed analysis of the added value in 12 organic supply chain examples. For this, interviews with market players and an “added value calculator” tool are employed.

The farm gate and retail price of organic products is higher than conventional. Supermarkets are the main outlet for organic milk and apples in the countries studied, whereas the situation for organic pasta is varied, suggesting that the use of different sale channels is influenced by the food product. The farmers’ share of added value ranges between 3 and 65 per cent of the added value created in the organic supply chains analysed. Organic offers opportunities to increase the farmers’ share of added value both in supermarkets and alternative sale channels, by developing collaboration, physical infrastructures for organic and integrating operations upstream of the chain.

While more research is needed into a larger number of chains, this paper indicates that there are dynamics and features at supply chain level, such as the distribution of added value and the target markets used, that cannot be interpreted according to the binary division between “mainstream” and “alternative” organic suggested by the conventionalisation hypothesis.

The distribution of added value for existing supply chains in eight European countries is calculated by using an effective added value calculator tool.

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.

Supply chains must rebuild for resilience to respond to challenges posed by systemwide disruptions. Unlike past disruptions that were narrow in impact and short-term in duration, the Covid pandemic presented a systemic disruption and revealed shortcomings in responses. This study outlines an approach to rebuilding supply chains for resilience, integrating innovation in areas critical to supply chain management.

The study is based on extensive debates among the authors and their peers. The authors focus on three areas deemed fundamental to supply chain resilience: (1) forecasting, the starting point of supply chain planning, (2) the practices of supply chain risk management and (3) product design, the starting point of supply chain design. The authors’ debated and pooled their viewpoints to outline key changes to these areas in response to systemwide disruptions, supported by a narrative literature review of the evolving research, to identify research opportunities.

All three areas have evolved in response to the changed perspective on supply chain risk instigated by the pandemic and resulting in systemwide disruptions. Forecasting, or prediction generally, is evolving from statistical and time-series methods to human-augmented forecasting supplemented with visual analytics. Risk management has transitioned from enterprise to supply chain risk management to tackling systemic risk. Finally, product design principles have evolved from design-for-manufacturability to design-for-adaptability. All three approaches must work together.

The authors outline the evolution in research directions for forecasting, risk management and product design and present innovative research opportunities for building supply chain resilience against systemwide disruptions.

Traditionally, philosophers have ascribed moral agency almost exclusively to humans (Eshleman, 2004). Early writing about moral agency can be traced to Aristotle (Louden, 1989) and Aquinas (1997). In addition to human moral agents, Aristotle discussed the possibility of moral agency of the Greek gods and Aquinas discussed the possibility of moral agency of angels. In the case of angels, a difficulty in ascribing moral agency was that it was suspected that angels did not have enough independence from God to ascribe to the angels genuine moral choices. Recently, new candidates have been suggested for non‐human moral agency. Floridi and Sanders (2004) suggest that artificially intelligence (AI) programs that meet certain criteria may attain the status of moral agents; they suggest a redefinition of moral agency to clarify the relationship between artificial and human agents. Other philosophers, as well as scholars in Science and Technology Studies, are studying the possibility that artifacts that are not designed to mimic human intelligence still embody a kind of moral agency. For example, there has been a lively discussion about the moral intent and the consequential effects of speed bumps (Latour, 1994; Keulartz et al., 2004). The connections and distributed intelligence of a network is another candidate being considered for moral agency (Allen, Varner & Zinser, 2000). These philosophical arguments may have practical consequences for software developers, and for the people affected by computing. In this paper, we will examine ideas about artificial moral agency from the perspective of a software developer.

The objective of this research is twofold. First, we aim at identifying the pairwise relationships between Lean Production (LP) practices and Industry 4.0 (I4.0) technologies. Second, based on these results, we propose a framework for Lean Automation (LA) implementation, in which I4.0 technologies are integrated into LP practices.

To achieve that, a cross-sector survey was performed with 147 manufacturers that are undergoing a LP implementation aided by novel information and communication technologies from I4.0. Multivariate data analysis was applied in order to underpin the proposed framework for LA.

Findings indicate that I4.0 technologies are positively correlated with LP practices, providing evidence to bear the proposition of a LA framework that can potentially overcome traditional barriers and challenges of a LP implementation.

As previous studies have approached LA implementation from a narrow perspective or including a limited set of LP practices and I4.0 technologies, the proposition of an integrated framework unfolds a wider range of synergistic implementations that may corroborate to a holistic approach for continuous improvement in the Fourth Industrial Revolution era.

Big data poses as a valuable opportunity to further improve decision making in supply chain management (SCM). However, the understanding and application of big data seem rather elusive and only partially explored. The purpose of this paper is to create further guidance in understanding big data and to explore applications from a business process perspective.

This paper is based on a sequential mixed-method. First, a Delphi study was designed to gain insights regarding the terminology of big data and to identify and rank applications of big data in SCM using an adjusted supply chain operations reference (SCOR) process framework. This was followed by a questionnaire-survey among supply chain executives to elucidate the Delphi study findings and to assess the practical use of big data.

First, big data terminology seems to be more about data collection than of data management and data utilization. Second, the application of big data is most applicable for logistics, service and planning processes than of sourcing, manufacturing and return. Third, supply chain executives seem to have a slow adoption of big data.

The Delphi study is explorative by nature and the questionnaire-survey rather small in scale; therefore, findings have limited generalizability.

The findings can help supply chain managers gain a clearer understanding of the domain of big data and guide them in where to deploy big data initiatives.

This study is the first to assess big data in the SCOR process framework and to rank applications of big data as a mean to guide the SCM community to where big data is most beneficial.

The purpose of this paper is to investigate the effect of decentralised control on finished goods inventory levels in a distribution system, and to identify the factors that determine the overall inventory level.

The authors' study is based on a mixed method approach using both a survey and semi‐structured interviews to assess inventory management practices and firm performance.

It was found that the common assumptions that distribution outlets or dealers are homogenous and that their behaviour is uniform in response to central control, such as the manufacturer's strategy, do not hold in practice. In fact, the authors show that under conditions of decentralised control, the inventories held at outlet level vary greatly around the aggregate inventory at overall manufacturer level and in this sense bear little resemblance to it. Amongst other conclusions, these findings provide a possible explanation for previous studies' inconclusive evidence on inventory reduction.

The authors' research is based on evidence from the automotive industry in the USA; future research may include a wider industry analysis and geographical scope.

The paper identifies how incentives and decision‐making structures at the outlet level need to be considered in order to derive decisions that are optimal at the supply chain level.

The paper extends the current literature on the determinants of inventory levels by using dealer‐level data, as opposed to manufacturer or firm‐level data in previous studies, thereby identifying possible causes for the previously inconclusive evidence on inventory levels in distribution systems.