Adaptive practices in SMEs: leveraging dynamic capabilities for strategic adaptation

PurposeA global pandemic, broken supply chains, workforce constraints, technological advancements in artificial intelligence, etc. illustrate the continual threats that SMEs face. Extending the dynamic capability concepts of sensing, seizing and transforming, this research investigates practices by which SMEs successfully adapt over time.Design/methodology/approachA comparative case study method was employed using a purposive sample of SMEs, consisting of three American firms and one Canadian firm.FindingsThree sets of organizational practices, termed adaptive practices, that underlie dynamic capabilities for successful adaptation were identified: (1) continuous learning and process improvement, (2) leveraging reciprocal relationships and (3) communicating effectively.Research limitations/implicationsThe selected cases are from two countries in North America. Using a qualitative, inductive process, the authors are able to identify patterns of actions within various organizations; however, they are not able to establish causality.Practical implicationsThis study provides practical guidance for leaders to take action to improve their SME's dynamic capabilities for adaptation through creating coherent bundles of specified adaptive practices.Social implicationsBetter understanding of how SMEs successfully adapt to high uncertainty and business viability threats can result in multidimensional (e.g. financial, emotional) and multi-level (individual, family, community), positive outcomes for societal stakeholders.Originality/valueThe findings of this study build on the literature of dynamic capabilities and organizational practices and provide a practical foundation for effective adaptation, labeled as adaptive practices.

A Novel Distribution for Representation of 6D Pose Uncertainty

The 6D Pose estimation is a crux in many applications, such as visual perception, autonomous navigation, and spacecraft motion. For robotic grasping, the cluttered and self-occlusion scenarios bring new challenges to the this field. Currently, society uses CNNs to solve this problem. The CNN models will suffer high uncertainty caused by the environmental factors and the object itself. These models usually maintain a Gaussian distribution, which is not suitable for the underlying manifold structure of the pose. Many works decouple rotation from the translation and quantify rotational uncertainty. Only a few works pay attention to the uncertainty of the 6D pose. This work proposes a distribution that can capture the uncertainty of the 6D pose parameterized by the dual quaternions, meanwhile, the proposed distribution takes the periodic nature of the underlying structure into account. The presented results include the normalization constant computation and parameter estimation techniques of the distribution. This work shows the benefits of the proposed distribution, which provides a more realistic explanation for the uncertainty in the 6D pose and eliminates the drawback inherited from the planar rigid motion.

Sustainability and authenticity: are they food risk relievers during the COVID-19 pandemic?

PurposeThe study jointly investigates sustainability and authenticity concepts in the food context during the COVID-19 outbreak with a fourfold objective: (1) understanding whether sustainability and authenticity are equivalent concepts in consumers' perceptions; (2) advancing knowledge on the role played by them about food frauds' perception; (3) investigating whether these concepts are considered as “risk relievers” by consumers, (4) comparing the concepts to understand which one has a greater weight on the consumer's perception.Design/methodology/approachThe study adopts a Combination of a Uniform and a shifted Binomial distribution (CUB models) on data gathered in Spain between June and August 2020 through an online questionnaire.FindingsThe findings reveal that: (1) consumers perceive sustainability and authenticity as different concepts in the food context and (2) as two important indicators of fraud protection of a product for consumers; (3) besides, authenticity is seen as a “risk reliever” in buying a food product, as well as sustainability, (4) although results underline high uncertainty in the latter case.Originality/valueBy considering that the COVID-19 outbreak seriously threatens food safety, security and nutrition, this research elucidates the relevant role of food sustainability and authenticity concepts as “risk relievers” in terms of food frauds and negative issues related to COVID-19.

Vehicle Interaction Behavior Prediction with Self-Attention

The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lane gap. In addition, the proposed model utilizes the sliding window algorithm to extract VC behavior information. Then the temporal characteristics of the three interactive features mentioned above will be caught by two layers of self-attention encoder with six heads respectively. Finally, target vehicle’s future behavior will be predicted by a sub-network consists of a fully connected layer and SoftMax module. The experimental results show that this method has achieved accuracy, precision, recall, and F1 score of more than 92% and time to event of 2.9 s on a Next Generation Simulation (NGSIM) dataset. It accurately predicts the interactive behaviors in class-imbalance prediction and adapts to various driving scenarios.

Main body

Recently, misinformation has increasingly impacted public discourse and public safety. From the COVID-19 pandemic to national elections, society is increasingly examining the negative impact of misinformation. Exposure to misinformation has been linked to conflicting perceptions of social, economic, and political issues, which leads to polarization, radicalization, and even acts of violence. While research has examined the development and spreading of misinformation, little has been done to examine the processes of being exposed to, and influenced by, misinformation. This paper uses Reinforcement Sensitivity Theory to examine the effect of individual differences in personality traits related to the behavioral inhibition system on the behavioral and cognitive response to exposure to misinformation online. Trait BIS was related to how much individuals positively engaged with misinformation, as well as intentions for activism and radicalism. These findings suggest that high uncertainty/anxiety may increase engagement with and influence by misinformation.

Cluster Enterprise Comprehensive Risk Assessment: Methodology Based on the Functional-Target Approach

The effectiveness of the unification of enterprises in the cluster is also associated with high uncertainty and risks. Thus, the development of theoretical approaches and methodological instruments for efficient risk management of enterprises under the conditions of cluster association is an urgent scientific task. The methodology of a comprehensive risk assessment of the cluster enterprise is based on the use of the approach for building a functional-target model of a cluster enterprise, and is reduced to the search for a response to the question: can an event change the value of a providing indicator in such a way that this will lead to a deterioration in the resulting indicator in each enterprise subsystem? Based on the results of forecasting external risks, it was established that the group of state and global risks, in particular, political, territorial and financial, is characterized by significant threats for the next 5 years for the studied cluster enterprises. We proposed and tested a methodology for a comprehensive assessment of the risks of cluster enterprises, based on a functional-target approach, according to which a cluster enterprise as a socio-economic system is considered as a set of three basic subsystems: management, production and financial and economic.

Intuitive global sourcing – a study of supplier selection decisions by apparel SMEs

PurposeThe purpose of this paper is to create knowledge on the intuitive global sourcing process applied by small and medium-sized enterprise (SME) managers.Design/methodology/approachThis study reports on qualitative inquiries with experienced sourcing managers from 10 SMEs in the textile industry. The study follows a three-step semi-structured interviewing process, allowing us to gradually unveil the detailed nature of the intuitive supplier selection process.FindingsNine of the 10 SMEs rely on a highly intuitive supplier selections process, where one supplier at a time is gradually taken into the exchange while testing the supplier’s behavior. The process consists of an early heuristics sub-process, which gradually switches over to a more advanced intuiting behavioral pattern-matching process.Practical implicationsMost OM/SCM research has treated global sourcing and supplier selection as a highly rational, analytical and deliberate optimization problem. This study uncovers a completely different, and frequently successful, intuitive process, which could inspire managers in companies of all sizes, faced with high uncertainty about global supplier selection decisions.Originality/valueIntuition has recently been adopted in the global sourcing literature. However, this study is the first to offer detailed insights into a predominantly intuitive global sourcing process, specifically as it is managed by SMEs.

The Impact of the COVID-19 Pandemic on Stock Exchanges

The current world health crisis is characterized by the speed of its spread and its scale, and causing a direct global destructive economic impact that is present in every area of the globe. In this context of high uncertainty, the financial markets, especially the stock exchanges, have witnessed a decline in double figures in a very short period of time. In this chapter, the authors analyze how the COVID-19 pandemic impacts all variables of significant interest to financial economists, market regulators, and investors. This impact will be examined taking into account measures taken by governments, such as cities lockdown, border closures, canceling public events, and stopping public transport in order to slow down and stop the pandemic.

A close up of daily temperature and moisture in two Mexican high-elevation forests

The scarcity of meteorological stations and the strong need for climatic information in alpine forests require the use of large-scale climatic algorithms but the lack of in situ information produces high uncertainty on their suitability. In this study, we used linear mixed models to study the topographic effect (elevation and aspect) and time variations (from hourly to monthly) on temperature (T) and relative humidity (RH) with a 5-year instrumental database. Furthermore, we compared climatic information from a geographical algorithm and our in-situ data. Our data covered two mountains (Tláloc-TLA and Jocotitlán-JOC, State of México), four elevation belts (from 3500 m to 3900 m a.s.l.), and two aspects (Northwest and Southwest). We found differences for average temperature (TLA = 7.56 °C ± 0.03 °C and JOC = 6.98 °C ± 0.02 °C), and relative humidity between mountains (TLA = 69.3% ± 0.12% and JOC = 72.5% ± 0.13%,). The most significant variables explaining T were the elevation (Δ= -0.36 °C by 100 m) and aspect, while the aspect was relevant for RH. May was the warmest month (9.50 °C ± 0.10 °C for average temperature) while September the wettest for both mountains (85.1% ± 0.30% and 87.4% ± 0.25 % RH, respectively). Despite the higher correlations between climatic sources (up to r = 0.83), the geographical algorithm overestimates T and underestimates RH. We propose that when climatic information from geographical algorithms is used in alpine forests, calibrations are needed whenever possible with in situ information.