Uncertain Context Research Articles

The Resilient Agility of Brand Voice in Volatile Environments

Abstract In an era marked by rapid change, the role of brand voice within brand communication has become increasingly vital to protecting brand identity, moving beyond traditional marketing approaches. This research introduces the concept of “resilient agility” of brand voice and examines it in a variety of uncertain contexts. Aiming to investigate the potential influence of volatile environments on brand voice within the context of brand communication, the author based her research on the examination of various case studies and as a result introduced two new models of projecting brand identity through brand communication, including one tailored for volatile environments. The author identifies the resilient agility of brand voice in projecting the essence of brand identity, consistency in core messaging, and transparency in addressing issues as key success factors for brand communication. This research enhances the understanding of the projection model of brand identity through brand communication, especially in volatile environments, offering valuable insights for marketers aiming to maintain brand integrity and develop lasting customer relationships amidst uncertainty.

Hidden lithic treasures. Research perspectives on museum collections of the Neolithic stone tools

Museum collections contain numerous finds that are considered low-quality data and do not attract the proper attention of researchers. The most common reason is the uncertain context of the finds or lack of other precise identification. The main aim of this paper is to increase the scientific value of so-called stray finds. The collection of the Neolithic stone tools from two museums was subjected to multifaceted analysis combined with detailed archival research. The results allowed us to address two important aspects. At the level of objects, the use of microscopic observations proved the complexity and prolonged use-life of the Neolithic tools. At the cultural level, the re-evaluation of stray finds introduces changes in the range of Neolithic settlements in SW Poland.

A scalable Deep Q-Learning approach for hot stamping process under dynamic control environment

Despite the significant advancements in Artificial Intelligence and its applications, traditional control techniques still dominate industrial and manufacturing processes. These techniques often rely on static setups and default values, which make them resistant to environmental changes. However, in dynamic and uncertain contexts, real-time process adaptation offers significant advantages in decision-making and control. In this research, we present a novel approach utilising Deep Reinforcement Learning for real-time dynamic parameter adaptation and autonomous control of processes in unpredictable environments. The effectiveness of our proposal is illustrated through the development and training of a Deep Q-Learning control system tailored for optimising a hot stamping process. The control model aims to minimise defect rates and enhance efficiency by reducing forming times during batch productions. Comparative analysis against conventional industry practices, characterised by static and non-adaptive time settings, validates the efficacy of our solution, with improvements of over 20% in the times, depending on the scenario considered. Experimental evaluation in a semi-industrial pilot setting not only demonstrates the adaptability of our approach but also highlights its potential scalability for diverse industrial applications.

Extended Certainty Factors utilizing Neutrosophic Logic

Addressing uncertainty is a significant challenge in the field of artificial intelligence (AI). AI systems often encounter uncertainty due to incomplete data, ambiguous information, or inherent unpredictability in specific situations. To tackle this uncertainty, various solutions have been developed. These solutions range from probabilistic approaches like Bayesian networks and Monte Carlo methods to fuzzy logic and neural networks. These strategies enable AI systems to model and make judgments amid ambiguity by assigning probabilities, dealing with imprecise data, or utilizing learning processes that adapt to changing and uncertain contexts. However, probabilistic methods have limitations when it comes to handling uncertainty. These limitations include assumptions of independence, computational complexity, difficulty in capturing subjectivity, and interpretability. To address these limitations, other methods have been proposed, such as the model of certainty factors. This model offers a framework for reasoning under uncertainty by assigning a numerical value to statements or propositions. Additionally, neutrosophic logic extends classical logic by incorporating the concept of indeterminacy through truth/indeterminacy/falsity-membership functions. In this paper, we propose a method for investigating how certainty factors adapt to a neutrosophic environment. This method contributes to the development of more robust and adaptable decision support systems capable of dealing with diverse uncertainty. Our method, introduced for the first time in related literature, addresses issues such as the limited handling of indeterminacy, the inability to address contradictions, and the limited binary representation of uncertainty that characterize the model of certainty factors. We will provide a clear illustration of these implications through an example that demonstrates the superiority of our suggested method over the traditional technique of certainty factors.

Identifying limit reference points for robust harvest control rules in fisheries management

Risk and uncertainty are intrinsic characteristics of natural resources that must be taken into account in their management. Harvest control rules (HCR) used to be the central management tool to control stock fisheries in an uncertain context. A typical HCR determines fishing mortality as a linear relationship of the biomass binding only when the biomass is above a critical risk value. Choosing the linear relationship and the risk value is a complex task when there is uncertainty because it requires a high level of data and an in-deep knowledge of the stock. This paper fully characterizes robust HCRs that explicitly include scientific uncertainty using the robust control theory approach. Our theoretical findings show that under uncertainty: i) Constant HCRs are not robust; ii) Robust HCRs show a steeper linear relationship between fishing mortality and biomass and a higher value of biomass to be consider at risk than non-robust HCRs. From the implementation viewpoint, we assume a three-sigma rule and show that robustness is achieved by selecting a fishing mortality such that its deviation from the fishing mortality target is twice the deviation of the biomass from the biomass target, and the critical value of the biomass (the point below which fishing should cease, or become as close to zero as possible) is half of the biomass associated with the maximum sustainable yield when this is the target.

The MAMCABM framework for the evaluation of mobility decision‐making problems: theory and practice

AbstractIn the constantly evolving transportation and mobility industry, objective and reliable decision‐support systems (DSS) are crucial for addressing complex issues such as transit planning, mode selection, and policy formulation. This paper presents multiactor multicriteria analysis plus agent‐based modeling (MAMCABM), a novel framework that combines multiactor multicriteria analysis (MAMCA) and agent‐based modeling (ABM) to provide a comprehensive DSS. MAMCA excels in facilitating stakeholder‐centric evaluations, while ABM, enhanced by data analytics, adeptly models intricate, interactive systems. The combination of MAMCA and ABM enhances adaptability and precision in decision making. This integration utilizes data analytics and optimization algorithms to provide solutions that consider multifaceted criteria and diverse stakeholder perspectives in dynamic and uncertain contexts. The study outlines the mathematical underpinnings of MAMCABM and offers a practical guide for its implementation. The framework's efficacy is demonstrated through an empirical investigation that addresses mobility challenges in the Brussels Capital Region of Belgium. Compared to the previous study, this approach leverages simulated quantitative data alongside qualitative judgments from stakeholders. The integration of a consensus‐reaching algorithm further enhances the robustness of outcomes and effectively addresses uncertainties.

Acceptance and Commitment Teletherapy for Mixed Anxiety and Depressive Symptoms During the COVID-19 Pandemic: A case Study

The present case details a successful treatment response to 21 sessions of Acceptance and Commitment Therapy (ACT) delivered via teletherapy for mixed anxiety and depressive symptomology during the COVID-19 pandemic. The client (“Natalie”), a 19-year-old, White, cisgender female, presented to a university training clinic reporting primary problematic anxiety consistent with a diagnosis of Generalized Anxiety Disorder. The therapist conceptualized Natalie’s anxiety through the ACT psychological flexibility model, wherein Natalie’s challenges appeared to be maintained by experiential avoidance and lack of committed action. This case highlights (a) the unique challenges of treating someone with pervasive anxiety amid the uncertain context of the COVID-19 pandemic, (b) repeated assessment of transdiagnostic processes to inform intervention focus, and (c) the process of ACT teletherapy. Symptom, process, and outcome measures taken bi-weekly throughout therapy suggested theoretically consistent and reliable treatment responses. Clinical implications of the case are discussed.

Cognitive mechanisms of learning in sequential decision-making under uncertainty: an experimental and theoretical approach.

Learning to make adaptive decisions involves making choices, assessing their consequence, and leveraging this assessment to attain higher rewarding states. Despite vast literature on value-based decision-making, relatively little is known about the cognitive processes underlying decisions in highly uncertain contexts. Real world decisions are rarely accompanied by immediate feedback, explicit rewards, or complete knowledge of the environment. Being able to make informed decisions in such contexts requires significant knowledge about the environment, which can only be gained via exploration. Here we aim at understanding and formalizing the brain mechanisms underlying these processes. To this end, we first designed and performed an experimental task. Human participants had to learn to maximize reward while making sequences of decisions with only basic knowledge of the environment, and in the absence of explicit performance cues. Participants had to rely on their own internal assessment of performance to reveal a covert relationship between their choices and their subsequent consequences to find a strategy leading to the highest cumulative reward. Our results show that the participants' reaction times were longer whenever the decision involved a future consequence, suggesting greater introspection whenever a delayed value had to be considered. The learning time varied significantly across participants. Second, we formalized the neurocognitive processes underlying decision-making within this task, combining mean-field representations of competing neural populations with a reinforcement learning mechanism. This model provided a plausible characterization of the brain dynamics underlying these processes, and reproduced each aspect of the participants' behavior, from their reaction times and choices to their learning rates. In summary, both the experimental results and the model provide a principled explanation to how delayed value may be computed and incorporated into the neural dynamics of decision-making, and to how learning occurs in these uncertain scenarios.

Towards dynamic decision-making in government as service organization: insights from systems thinking

Purpose Service organizations operate in an increasingly complex and uncertain context that makes decision-making challenging. Despite well-recognized changes in the operational context of government as service organization, service literature has given surprisingly limited attention to what these changes imply for organizational decision-making. This study aims to face with the lack of fit of decision-making theorizing with the reality, within which most service practitioners operate, in order to foster the relevance of decision-making in service research and properly approach the false assumptions and misguided instructions for action. Design/methodology/approach To rectify the situation, the purpose of this paper is to advance a more holistic understanding of decision-making in government as service organization. The authors do so by reviewing the sparse, though insightful, prior literature on decision-making in service research and identifying four foundational assumptions of decision-making in the service context, that radically differ from the traditional assumptions of decision-making within the wider management literature. Findings The authors contribute to service research by further advancing the emerging dynamic understanding of decision-making by developing eight systems thinking-informed research propositions and a connected research agenda. In doing so, the paper offers the essential ground work that can revitalize the field of service management and equip it for facing the challenges that government as service organization is encountering in the 21st century. Originality/value The formulated eight research propositions demonstrate that decision-making in a government as service organization occurs within complex adaptive systems composed of multiple subsystems and is characterized by a high degree of unpredictability. It is a process influenced by multiple actors part of the system and subsystems, through multiple feedback loops, where the implications of prior decisions inform the future decisions.

The Role of Forecasts in Planning for Energy Infrastructure: A Historical Look at Past Futures in Postwar Quebec

The Role of Forecasts in Planning for Energy Infrastructure: A Historical Look at Past Futures in Postwar Quebec

The effect of group size and task involvement on temporal binding window in clap perception.

We collect various types of information from our environment and organise it to create a coherent representation. Several researchers have suggested that multiple signals within the temporal binding window (TBW) can be integrated into a single coherent experience, such as flashes, beeps, and the McGurk effect. However, there is no evidence that TBW distortion also occurs in group interactions. This study investigates the influence of group size (i.e. the group size effect) and the degree of task involvement in temporal perception using computer-generated clap sound experiments. Participants listened to the randomly generated clap sounds and evaluated whether they were synchronised. We established three conditions based on different levels of task involvement: low (L), middle (M), and high (H) conditions. The varying task involvements reflect how participants interact with the clap sounds, ranging from passive listening in the L condition to actively generating sounds by pressing a key in the M condition, or attempting to synchronise key pressing sounds with the sounds in the H condition. Our experiments show a robust group size effect on TBW, regardless of the different conditions. In other words, as the group size increases, participants perceive the group clap as a single event. Furthermore, we found that the uncertain cause-effect relationship condition (H condition) shows the highest TBW. Interestingly, the TBW in the rigid cause-effect relationship (M condition) is the same as that in the no involvement condition (L condition). Our results suggest that a widened TBW in collective behaviour may facilitate cohesive action, enabling individuals to adapt to the group in relatively uncertain contexts.

Exploring the impact of smartphone addiction on decision-making behavior in college students: an fNIRS study based on the Iowa Gambling Task.

The pervasive use of smartphones, while enhancing accessibility to information and communication, has raised concerns about its potential negative effects on physical and mental health, including the impairment of decision-making abilities. This study investigates the influence of smartphone addiction on decision-making in college students. A sample of 80 individuals aged 17 to 26 was selected and divided into two groups based on their Smartphone Addiction Scale-Short Version (SAS-SV) scores. Participants underwent the Iowa Gambling Task (IGT) to evaluate their decision-making in risky and uncertain conditions, while fNIRS recorded their prefrontal cortex activity. The study found that individuals prone to smartphone addiction tend to make riskier choices in risky situations. However, when faced with decisions based on ambiguity, the smartphone addiction group showed increased brain activity in the dlPFC (specifically in channels 4, 9, and 11) compared to when making risky decisions. Despite this increased brain activation, there was no observable difference in behavior between the addiction-prone and control groups in ambiguous scenarios. Notably, the left dlPFC (e.g., channel 4) exhibited significantly higher activation in the addiction group compared to the control group. Findings suggest that smartphone addiction can detrimentally influence decision-making, behaviorally and neurologically, particularly in uncertain contexts. This study supports the classification of smartphone addiction as a genuine addiction and underscores its significance in psychiatric research. In essence, our research underscores the adverse effects of excessive smartphone use on decision-making processes, reinforcing the necessity to treat smartphone addiction as a pressing public health issue.

Inconsistency elimination of multi-source information fusion in smart home using the Dempster-Shafer evidence theory

In the smart home, inconsistencies in multi-source information often occur due to the vast quantities and diverse varieties of context information, and the Dempster-Shafer (D-S) evidence theory exhibits remarkable efficacy in addressing this issue. However, the use of basic possibility assignment (BPA) to quantify the credibility of information may introduce the uncertainty contained in the original data. In order to obtain a more reliable BPA and improve the accuracy of multi-source information fusion, an index that combines the relationships of sources and the information volume of context (RSIVC) is defined to comprehensively calculate the uncertainty of context information. In addition, interval distance is introduced to calculate the credibility of the proposition (CoP), which quantifies the correspondence among uncertain context information and all propositions in the frame of discernment (FoD). Finally, based on the d-S evidence theory, a multi-discount context information inconsistency elimination (MDCIE) algorithm using RSIVC and CoP is proposed, which is specifically applied to address the problem of inconsistency in multi-source light intensity information in smart homes. In the experimental part, we compare the traditional and state-of-the-art inconsistency elimination algorithms to verify the effectiveness of the proposed algorithm. Besides, we also test the efficiency of various components of the MDCIE algorithm as well as several RSIVC indicator-building strategies to ensure that the proposed algorithm is optimal. Under the conditions of high precision, medium precision, and low precision of sensors, the context-judge rate reaches 99.24 %, 98.58 %, and 98.09 %, respectively, which corroborates that the MDCIE algorithm is highly effective.

Robust two-stage optimization consensus models with uncertain costs

In the consensus-reaching process (CRP), decision-makers (DMs) frequently encounter the dilemma of too much uncertain information, which can lead the actual decision to deviate from the optimal solution obtained by the currently used consensus models. To do this, we construct two robust two-stage optimization consensus models with uncertain costs and obtain their robust two-stage counterparts. We then apply a Benders decomposition algorithm to solve the resulting models. Finally, the experimental results show that the new models are better suited for uncertain contexts and could help DMs produce more reliable choices.

How does Human Resource Management help service organizations to thrive in uncertainties and risks: Postcrisis as a context

AbstractWith heightened uncertainties and risks in the fluctuating business environment, existing studies have concentrated on elucidating how service organizations leverage human resource practices to adapt to and survive such unforeseen and disruptive threats. However, how such practices could serve the strategic objective of cultivating a sustainably thriving workforce across different situations is not well understood. Thriving is a core transitional state that fosters positive behaviors, such as creative customer‐related problem‐solving. Applying social information processing theory, we propose and test a model by exploring how organizations that engage in thriving‐oriented human resource management (HRM) encourage employees to take responsibility and promote constructive change, thereby activating their creative problem‐solving behaviors. Specifically, we theorize and develop measures to promote thriving‐oriented HRM in Study 1. In Study 2, we collected multisource and multi‐wave data from 296 frontline service employees and 45 supervisors in China. Our findings reveal that thriving‐oriented HRM is positively related to felt responsibility for change, which ultimately encourages creative problem‐solving. We also show that the threat imposed by a crisis, that is, the COVID‐19 pandemic, strengthens the positive relationship between thriving‐oriented HRM and felt responsibility for change. Our study contributes to the HRM literature, especially on thriving‐oriented HRM and employee perception, and has practical implications for service organizations in the uncertain context.

What works in improving inter-agency responses to missing children investigations: A scoping review

Responsibility for responding to missing children belongs to multiple agencies, including police, children’s homes and social services, but evidence highlights issues with collaboration. The following scoping review seeks to identify what is currently known about mechanisms that enhance collaboration in responding to missing. Findings highlight the value of (i) information-sharing techniques; (ii) cross-agency technology; (iii) single points of contact; (iv) regular multi-agency meetings; (v) shared understanding of terminology; (vi) clarifying roles and responsibilities; and (vii) joint training. However, research is needed that empirically tests the effectiveness of strategies and interventions for improving inter-agency working in this risky and uncertain context.

Impacts of rangeland ecological compensation on livelihood resilience of herdsmen: an empirical investigation in Qinghai Province, China

The increasing use of the payment for ecosystem services (PES) represents a prominent institutional shock triggered by external interventions on livelihoods of natural resource managers. To reconcile the needs for both conservation and development, a deep understanding on the influence mechanisms of PES on farmer livelihood resilience is paramount, particularly in dynamic and uncertain contexts. Taking the Rangeland Ecological Protection Subsidy and Reward scheme that has been implemented in pastoral China since 2011 as a case study, the paper empirically investigates the impacts of PES on livelihood resilience of livestock farmers in Qinghai Province. Based on a comprehensive evaluation of herder livelihood resilience from three dimensions—buffering, self-organizing and learning capacities, multiple linear regression was applied to examine the PES impacts. Resilience enhancement was mainly found in buffering capacity for herders with a medium to large herd size. Results of univariate parallel mediation models further reveal that household income growth and diversification and livestock upsizing were three key intermediary processes that mediated the enhancement and together contributed 28.83% of the total effect. The findings help narrow a knowledge gap in understanding whether, how and for whom PES contributes to improve farmer livelihood resilience. To achieve “win-win” outcomes, we stress that greater attention should be given to tailoring design and implementation of PES programs to align with the specificity of local contexts.

Contingency Reversal in Conditioned Fear Learning: The Moderated Mediation Model of Intolerance of Uncertainty and Instruction.

The study aimed to examine the roles of anxiety and intolerance of uncertainty (IU) in conditioned fear learning under an uncertain context induced by the contingency reversal of the association between the conditioned stimulus and the unconditioned stimulus (CS-US). The study sample comprised 53 participants, randomly divided into two groups: a non-instruction group and an instruction group. The experimental procedure encompassed five stages: pre-acquisition, acquisition, generalization, reversal acquisition, and reversal generalization. Our study primarily focused on analyzing a moderated mediation model. In the instructed group, we observed that the reversed fear generalization response was directly influenced by the pre-reversal fear generalization response, while also being indirectly mediated by the IU factor. However, in the non-instructed group, we did not find a significant mediating effect of IU. Moreover, we noted that the mediation of IU was contingent on the instructional information. It is noteworthy that anxiety did not exhibit a discernible role in conditioned fear within the uncertainty condition in our study. The findings provide novel insights into fear-related phenomena, emphasizing the intricate interplay between individual traits and fear generalization under conditions of uncertainty. They contribute to understanding the mechanisms of emotional and cognitive interactions in uncertain conditions.

Timing of preventive behavior in the case of a new and evolving health risk: the case of COVID-19 vaccination

BackgroundTime preferences for preventive behavior under novel risks and uncertain contexts may differ from timing preferences related to familiar risks. Therefore, it is crucial to examine drivers of preventative health behavior timing in light of new health risks. Using the case of COVID-19, we examine factors affecting vaccination timing plans when vaccines were widely available in the European Union (EU).MethodsWe use data from the Flash Eurobarometer 494 survey (May 21–26, 2021), which collected information on EU residents’ attitudes towards COVID-19 vaccinations. We also use the ‘Our World in Data’ vaccination database for country-level COVID-19 vaccination rates. Probit regressions were conducted to determine how local vaccination rates, trust in information sources, social norms, vaccine safety beliefs, and risk understanding affected the probability of COVID-19 vaccination delay.ResultsOf total participants (n = 26,106), 9,063 (34.7%) were vaccinated, 7,114 (27.3%) wanted to get vaccinated as soon as possible, 5,168 (19.8%) wanted to delay vaccination and 2,962 (11.4%) resisted vaccination. Participants were more likely to delay COVID-19 vaccination if they lived in a country with lower vaccination prevalence, trusted online social networks, family, friends, and colleagues for vaccination information, were eager to follow vaccination-related social norms, expressed vaccine safety concerns, and understood the risk of catching COVID-19 without a vaccine to be lower.ConclusionsResults from the study contribute to understanding important factors that predict timing of vaccination plans. These findings can also contribute to the wider knowledge base about timing of preventive behavior uptake in novel risk contexts.

Adoption of New Technology Vaccines

Extensive research has examined the diffusion of innovations for products that can be trialed, and where the most adverse outcome, if a product fails, is a financial loss. However, less research has explored consumer responses to innovations in highly uncertain contexts characterized by health losses, lack of trialability, and the opportunity to free ride on other's adoption. This research focuses on vaccine decision making as a unique case within such contexts and extends the findings to other domains. Four studies (Ntotal = 1,796; five supplementary studies, Ntotal = 643) test the propositions of a formal model that incorporates uncertainty and others' choices into the adoption decision. The results show that consumers are surprisingly averse to products that are described as employing a new technology (e.g., mRNA technology) and require an “efficacy premium” to compensate for higher perceived uncertainty. However, considerable heterogeneity exists due to individual differences in technology readiness, trust in government, and risk attitudes. Notably, despite the prominent threat of free riding, a social proof nudge (communicating increasing population adoption) effectively reduces aversion to new technology. In this context, social proof information does not merely drive conformity or social learning, but instead increases adoption of new technology by alleviating perceived uncertainty.