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This study aims to examine the impact of investment in innovation, market structure and competitive position on the supermarket value chain in the USA.

This paper used secondary data from Economatica® for the period 2010–2020, encompassing 83 firms within the US supermarket value chain, resulting in 913 observations for each model covariate. The data were analyzed through structural equation modeling, using the partial least squares (PLS) approach, using SMARTPLS® software.

The results indicate that both market structure and investment in innovation have a significant impact on a firm’s competitive position. Specifically, a more concentrated market structure is associated with a weaker competitive position, whereas higher levels of investment in innovation correspond to a stronger competitive position. However, no significant relationship was found between investment in innovation and market structure.

This study is limited to the Economatica® database, from which this paper selected a sample of firms within the US supermarket value chain. This paper recommend future research to compare data from other countries or sectors. For the three-dimensional model, this paper used constructs with indicators of investment, performance and market concentration; however, this paper suggests incorporating additional variables to enhance the model’s robustness.

An analysis of market structure and its impact on competitive positioning offers valuable insights for managers regarding the strategic dimensions of the supermarket industry. Integrating the effects of investment in innovation into business strategies can enhance long-term economic benefits. Adopting a value chain perspective further facilitates the examination of market equilibrium and competitive positioning within the supermarket industry.

The findings of this study can help prevent the adoption of misguided strategies that fail to foster business growth. By highlighting the significant impact of innovation on performance, while considering the competitive dynamics within the supermarket industry, the study provides a deeper understanding of the risks faced by various firms across the value chain and the dual nature of competition within the sector. Moreover, it reinforces the relevance of research on economic sustainability and supports the development of effective growth strategies for firms.

While most studies focus on hypotheses examining the relationships between innovation and competitive positioning or between innovation and market structure, this study introduces a three-dimensional model that uses structural equation modeling to test these interconnections simultaneously. Furthermore, it extends the analysis by incorporating the value chain as a central component, using a diverse sample of firms directly linked to the core operations of the supermarket industry.

The aim of this study was to evaluate the performance of fuel flow processes in a network of eight gas stations, located in the mesoregion of Alto Paranaíba and Triângulo Mineiro.

Two multi-criteria decision support methods were applied, respectively, of a statistical and mathematical nature, namely, Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA). The research method used was quantitative, with a brief complement of qualitative research, and descriptive in purpose, supported by the inductive method. The data collection stage took place with the support of interviews, with the application of a structured questionnaire, and non-probabilistic sampling, for convenience.

It was possible to verify that the gas station that stood out the most was station 2 (GS2), which achieved maximum efficiency, a fact that can be justified by the analysis resulting from the application of PCA, as for the product purchase variable (PP), the GS2 is the one that buys the most fuel, and is also the one with the largest storage capacity (C), and the highest volume of product sales (PS), which suggests signs of balance between supply and demand for this station, justifying its prominence.

The limitations of the study were related to the DEA technique, which requires a number of variables/indicators three times smaller than the number of DMUs considered, and the difficulty in obtaining financial data on the DMUs analyzed. Considering the security and anonymity of the gas station network, it was not possible to use this data.

The performance assessment of fuel flow processes carried out in this study promotes the efficient use of available resources as well as identifying efficient DMUs that represent benchmarks for improving management processes and performance of inefficient DMUs.

From a social perspective, this study promotes the improvement of the quality of flow processes and effective management of the fuel supply chain, ensuring the safe storage and transportation of fuels to customer supply. Performance management in this sector moves other sectors of the economy, since an efficient unit represents a balance between supply and demand, and consequently, boosts the regional economy, promoting economic growth of the population. Hiring qualified labor for this purpose also represents one of the implications of the study. From an environmental perspective, optimizing flow processes generates a reduction in greenhouse gas emissions and encourages the formulation of public policies aimed at consolidating sustainable practices.

Performance management applied to the context of the fuel supply chain is a relevant topic that has been little explored in scientific research, with a low level of information detail. This study using the inductive method allows the generalization and replication of this management pattern in other organizations in the sector in order to increase the efficiency of the fuel distribution system, with the perspective of maximizing outputs and reducing input consumption. In this aspect, the study introduces possibilities for advancement in social and environmental perspectives based on the effective management of fuel logistics.

Prior research has found that firms' adoption of digital technologies (i.e. digitalization) enhances transaction efficiency and improves firm performance. However, this finding is based on the assumption that firms respond to consumers' adoption of digital technology (market digitalization) in a timely fashion. The study investigates the impact of market digitalization on firm performance in Latin America, where resistance to change is often higher, despite the positive impact on performance when companies respond to the environmental shock of digitalization by restructuring.

Using data from seven Latin American countries from 1997 to 2018 (Argentina, Brazil, Chile, Colombia, Peru, Venezuela and Mexico), fixed-effects panel regression robustly supports the results.

Most Latin American firms fail to capitalize on the benefits of market digitalization, and their performance declines as a result. The authors extend research on digitalization by incorporating theoretical insights from the restructuring literature, finding that implementing a substantial restructuring strategy is a viable way to overcome market digitalization.

The authors demonstrate that the digitalization–firm performance relationship is more complex than has been described in studies using samples from developed economies. The authors establish restructuring as an effective adaptation strategy in Latin America, although the institutional environment's characteristics may constrain or discourage firms from adopting it.

Diversified trading networks have recently drawn a great deal of attention. In the process, the importance of diversity has perhaps been overemphasized. Using the trade in port wine from Portugal to Britain as an example, this essay attempts to show how a market once dominated by general, diversified traders was taken over by dedicated specialists whose success might almost be measured by the degree to which they rejected diversification to form a dedicated “commodity chain.” The essay suggests that this strategy was better able to handle matters of quality and the specialized knowledge that port wine required. The essay also highlights the question of power in such a chain. Endemic commodity-chain struggles are clearest in the vertical brand war that broke out in the nineteenth century, which, by concentrating power, marked the final stage in the transformation of the trade from network to vertical integration.

The SARS-CoV-2 pandemic has caused a major impact on worldwide public health and economics. The lessons learned from the successful attempts to contain the pandemic escalation revealed that the wise usage of contact tracing and information systems can widely help the containment work of any contagious disease. In this context, this paper investigates other researches on this domain, as well as the main issues related to the practical implementation of such systems and specifies a technical solution.

The proposed solution is based on the automatic identification of relevant contacts between infected or suspected people with susceptible people; inference of contamination risk based on symptoms history, user navigation records and contact information; real-time georeferenced information of population density of infected or suspect people; and automatic individual social distancing recommendation calculated through the individual contamination risk and the worsening of clinical condition risk.

The solution was specified, prototyped and evaluated by potential users and health authorities. The proposed solution has the potential of becoming a reference on how to coordinate the efforts of health authorities and the population on epidemic control.

This paper proposed an original information system for epidemic control which was applied for the SARS-CoV-2 pandemic and could be easily extended to other epidemics.

The purpose of this paper is to present the Moore-Penrose pseudoinverse (PI) modeling and compare with artificial neural network (ANN) modeling for switched reluctance machine (SRM) performance.

In a design of an SRM, there are a number of parameters that are chosen empirically inside a certain interval, therefore, to find an optimal geometry it is necessary to define a good model for SRM. The proposed modeling uses the Moore-Penrose PI for the resolution of linear systems and finite element simulation data. To attest to the quality of PI modeling, a model using ANN is established and the two models are compared with the values determined by simulations of finite elements.

The proposed PI model showed better accuracy, generalization capacity and lower computational cost than the ANN model.

The proposed approach can be applied to any problem as long as experimental/computational results can be obtained and will deliver the best approximation model to the available data set.

In reinforced concrete (RC) structures, an evidence of damage is the presence of cracking. In order to evaluate the effect of damage on cracking pattern and natural frequency in RC slabs, two of such structures with different dimensions and reinforcement ratios were tested, in which cracks were induced through application of static load, followed by modal tests using impact excitation. The paper aims to discuss this issue.

The gradient of the fundamental natural frequency along the decay, the crack opening rate and also a global damage index based on changes of the fundamental natural frequency were evaluated.

The behaviour of the aforementioned gradient was distinct for both slabs, increasing monotonically with the cracking level for the slab with lowest reinforcement ratio, and increasing until 33 per cent of the collapse load and then decreasing afterwards for the slab with the highest ratio. Changes of the gradient were consistent with changes of the crack opening rate. Both results of gradient changes and cracking pattern brought evidence that the balance between open (old) and breathing (new) cracks differed between the slabs, and may be responsible for such differences.

Damage assessment in RC structures using vibration tests is mostly concentrated on beams. In this work, an advance is made by investigating slabs. The lack of a unique pattern of changes of the gradient implies that its absolute value is not generally suitable for the association with the damage level. However, the impact tests can be effectively used to detect early damage on slabs using this proposed parameter.