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This study aims to design and validate a theoretical model for capacitive imaging (CI) sensors that incorporates the interelectrode shielding and surrounding shielding electrodes. Through experimental verification, the effectiveness of the theoretical model in evaluating CI sensors equipped with shielding electrodes has been demonstrated.

The study begins by incorporating the interelectrode shielding and surrounding shielding electrodes of CI sensors into the theoretical model. A method for deriving the semianalytical model is proposed, using the renormalization group method and physical model. Based on random geometric parameters of CI sensors, capacitance values are calculated using both simulation models and theoretical models. Three different types of CI sensors with varying geometric parameters are designed and manufactured for experimental testing.

The study’s results indicate that the errors of the semianalytical model for the CI sensor are predominantly below 5%, with all errors falling below 10%. This suggests that the semianalytical model, derived using the renormalization group method, effectively evaluates CI sensors equipped with shielding electrodes. The experimental results demonstrate the efficacy of the theoretical model in accurately predicting the capacitance values of the CI sensors.

The theoretical model of CI sensors is described by incorporating the interelectrode shielding and surrounding shielding electrodes into the model. This comprehensive approach allows for a more accurate evaluation of the detecting capability of CI sensors, as well as optimization of their performance.

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

Sports marketing and sponsorship research is located at the intersection of behavioral and design research, which means that it analyzes the current world and shapes a future world. This dual focus poses challenges for formulating and testing theories of sports marketing.

This article develops criteria for categorizing theoretical concepts as either behavioral or formed as different ways of expressing ideas of sports marketing research. It emphasizes the need for clear concept categorization for proper operationalization and applies these criteria to selected theoretical concepts of sports marketing and sponsorship research.

The study defines three criteria to categorize theoretical concepts, namely (1) the guiding idea of research, (2) the role of observed variables, and (3) the relationship among observed variables. Applying these criteria to concepts of sports marketing research manifests the relevance of categorizing theoretical concepts as either behavioral or formed to operationalize concepts correctly.

This study is the first in sports marketing to clearly categorize theoretical concepts as either behavioral or formed, and to formulate guidelines on how to differentiate behavioral concepts from formed concepts.

This study aims to explore the direct and indirect effects of sales opportunism and sales conflict as well as of non-economic and economic satisfaction in business-to-business (B2B) sales relationships. This understanding offers B2B buyers enhanced knowledge of sales business expectations towards sustainable business relationships in the future.

Through self-administered questionnaires, data were obtained from 237 sales or marketing managers/directors of small- and medium-sized companies across industries in Spain, who were randomly contacted via LinkedIn. The multivariate analysis of measurement and structural models was based on IBM SPSS Amos 27.

The study confirms that sales opportunism positively affects sales conflict. Moreover, sales opportunism is negatively associated with non-economic sales satisfaction, whereas non-economic sales satisfaction is positively associated with economic sales satisfaction. Consequently, if all associates are pleased with the relationship and the gains it can provide, a long-standing orientation can be achieved.

The study expands existing theory on seller–buyer relationships in a B2B context. It contextualises direct and indirect relationships between two antecedents (sales opportunism and sales conflict) and two postcedents (economic sales satisfaction and non-economic sales satisfaction) in sales business–buyer settings.

The study guides buyers in B2B relationships towards an improved understanding of how sales businesses perceive opportunism and conflict (as negative precursors) to impact non-economic satisfaction and how it can influence economic satisfaction.

Most studies explore B2B relationship building from the perspective of the buyer, thereby creating a shortfall in developing an understanding of all partner expectations in B2B relational intent. Moreover, the measurement of satisfaction as a multidimensional construct secured the integration of non-economic satisfaction and economic satisfaction within a single model allowing the constructs measured in this study to be holistically assessed.

Amidst unpredictable and turbulent periods, such as the COVID-19 pandemic, service organization’s responses are required to be innovative, adaptable and resilient. The purpose of this study is to explore the utilization of both reconfiguration and transformational strategies as instruments for cultivating resilience and advancing sustainability in service organizations.

The study examines a proposed resilience model using fuzzy logic. The research also used a semantic differential scale to capture nuanced and intricate attitudes. Finally, to augment the validity of the resilience model, a measurement scale was formulated using business mathematics and expert opinions.

Although investing in resilience training can help organizations gain control and maintain their operations in times of crisis, it may not directly help service organizations understand the external turmoil, seek available resources or create adaptive remedies. Conversely, high levels of reconfiguration and transformation management vigour empower a service organization’s revolutionary, malleable vision, organizational structure and decision-making processes, welcoming talented and innovative employees to enhance capabilities during crises.

The resilience model bestows a comprehensive understanding of the pertinence of building resilience for service organizations identifying the antecedents that influence the adoption of these strategies and introduces a range of theoretical perspectives that empowers service organizations to conceptualize and plan for building resilience. The research guides service organizations to become more resilient to external shocks and adapt to changing circumstances by diversifying their offerings, optimizing their resources and adopting flexible work arrangements. The study elaborates on the enhancement of resilience, increasing innovation, improving efficiency and enhancing customer satisfaction for service organizations to remain competitive and contribute to positive social and economic outcomes through the adoption of both reconfiguration and transformational strategies.

The study also guides the service organizations to become more resilient to external shocks and adapt to changing circumstances by diversifying their offerings, optimizing their resources and adopting flexible work arrangements. Rapid innovation and business model innovation are essential components, enabling service organizations to foster a culture of innovation and remain competitive. In addition, the adoption can lead to improved financial performance, job creation and economic growth, contributing to positive social and economic impacts.

The resilience model bestows a comprehensive understanding of the pertinence of building resilience for service organizations. It identifies the antecedents that influence the adoption of these strategies and introduces a range of theoretical perspectives that empowers service organizations to conceptualize and plan for building resilience. The research also provides a foundation for further investigation into the effectiveness of these strategies and their impact on organizational performance and sustainability. By better preparing service organizations for disruptions and uncertainties, this research triggers ameliorated organizational performance and sustainability.

Within the realm of the service industry, the present investigation has undertaken the development, quantification and scrutiny of both resilience and tenacity. In addition, it has delved into the intricate dynamics surrounding the influencing factors and antecedents that bear upon resilience, elucidating their consequential impact on the operational performance and outlook of service-oriented organizations. The findings derived from this research furnish valuable insights germane to enhancing operational efficacy and surmounting impediments within the sector.

A quantitative theoretical model is proposed to measure how productivity performance can be affected by strategic decisions related to specific competitive priorities.

This study proposes the Primary Transformation Model (PTM) and an equation to measure cause-and-effect relationships between productivity and competitive priorities.

The interdependence between productivity and competitive priorities was studied using the PTM and the proposed model indicates that strategies that improve external performance also impact internal productivity. It was also observed that the compatibility between competitive priorities depends on the initial manufacturing conditions and the implementation method adopted.

The proposed model is theoretical and, as such, is an abstraction of reality and does not consider all possible aspects. It consists of a novel approach that still requires further empirical testing. The PTM provides insights about the trade-offs between productivity and strategic objectives, as well, contributes to the ongoing research on manufacturing strategy and can be further developed in future studies.

The main practical implication is to allow companies to relate their strategic decisions to their productivity performance.

This research also contributes to societal issues by enabling firms to better align strategic objectives and operations, which ultimately allows offering products more suited to the needs of customers, thus making better use of the required resources and favoring economic growth.

The model proposed allows objective assessment of actions aiming at operational efficiency and effectiveness, in addition to providing insights into cause-and-effect relationships between productivity and competitive priorities. The model can also be used in empirical investigations on manufacturing strategy.

This paper develops and tests a theoretical model that draws on the Diffusion Contingency Model and the Theory of Human Behavior to explain the factors influencing users’ acceptance of accrual accounting in terms of two distinct dimensions: behavioral intention and usage behavior.

Based on surveyed data from financial departments and directorates of different Greek general government entities, the paper uses factor analysis to build a theoretical model that assesses the factors influencing behavioral intention to adopt and usage behavior of accrual accounting. Then, it tests the relationship between behavioral intention and usage behavior through structural equation modeling.

The theoretical model suggests that the expected improvement of the quality of financial information and political and financial support are the most important determinants of behavioral intention. Usage behavior is mainly influenced by the compatibility between the existing legal framework and the new accounting system. The structural equation modeling identifies a statistically significant positive influence of behavioral intention on usage behavior.

The study provides valuable insights regarding the timing and focus of the actions taken by policymakers when designing accounting reforms. Special attention is drawn to the factors influencing behavioral intentions, as these are found to influence usage behavior significantly.

The study extends prior research on the diffusion of accounting innovations by breaking down the diffusion process into intentions-oriented actions aiming to promote accrual accounting and increase acceptance and implementation-oriented actions aiming to facilitate successful implementation.

Digital labor platforms (DLPs) are transforming the nature of the work for an increasing number of workers, especially through extensively employing automated algorithms for performing managerial functions. In this novel working setting – characterized by algorithmic governance, and automatic matching, rewarding and punishing mechanisms – gig-workers play an essential role in providing on-demand services for final customers. Since gig-workers’ continued participation is crucial for sustainable service delivery in platform contexts, this study aims to identify and examine the antecedents of their working outcomes, including burnout and engagement.

We suggested a theoretical framework, grounded in the job demands-resources heuristic model to investigate how the interplay of job demands and resources, resulting from working in DLPs, explains gig-workers’ engagement and burnout. We further empirically tested the proposed model to understand how DLPs' working conditions, in particular their algorithmic management, impact gig-working outcomes.

Our findings indicate that job resources – algorithmic compensation, work autonomy and information sharing– have significant positive effects on gig-workers’ engagement. Furthermore, our results demonstrate that job insecurity, unsupportive algorithmic interaction (UAI) and algorithmic injustice significantly contribute to gig-workers’ burnout. Notably, we found that job resources substantially, but differently, moderate the relationship between job demands and gig-workers’ burnout.

This study contributes a theoretically accurate and empirically grounded understanding of two clusters of conditions – job demands and resources– as a result of algorithmic management practice in DLPs. We developed nuanced insights into how such conditions are evaluated by gig-workers and shape their engagement or burnout in DLP emerging work settings. We further uncovered that in gig-working context, resources do not similarly buffer against the negative effects of job demands.

The purpose of this paper is to evaluate the causal relationship between Lean practices and team performance. Specifically, the authors tried to demonstrate which practices act as enablers of continuous improvement and waste elimination and what is their impact on team performance.

A survey was carried out in a Higher Education Institution (HEI) in Southern Brazil. The authors obtained a sample of 785 respondents. The data analysis procedures involved confirmatory factor analysis and structural equations modeling.

The results of the research provided support for the positive influence of continuous improvement on waste elimination and of these two practices on team performance. In addition, empirical support was obtained for the effect of leadership support, employee involvement and internal process customers on continuous improvement.

Data collection was carried out online, so we were not able to maintain full control of the research respondents. This research generates relevant insights for decision-makers in the HEI environment, especially concerning Lean practices and team performance. The effects analyzed are even more relevant given the pandemic context.

This study shows how some higher education Lean practices can positively affect continuous improvement and better team performance. The results raise important insights for decision-makers to offer better higher education public services, especially given the context and changes imposed by the pandemic situation.

This paper initiates the discussion about enablers of continuous improvement and waste elimination in HEI and demonstrates their impact on team performance.

The conflict between institutionalism and neoclassicism in the 20th century has been investigated by scholars over the years. Many of them believe that in the postwar period, neoclassicism triumphed while institutionalism largely disappeared. The present chapter takes a very different view. The late 20th century represents a broad synthesis of neoclassical and institutional themes in a methodology we call pragmatic empiricism. That approach combines the mathematical model building and theoretical formalism of neoclassical economics with the institutional economist’s data-driven statistical analysis and concern for developing institutional forms. We use as a case study the history of American locational economics from the 1930s to the present. The mixing of institutional and neoclassical themes is quite evident in the work of three young scholars at Harvard who effectively initiated American locational economics. In the postwar period, we find a series of outstanding, well-published papers that capture the spirit of the “founders.” These papers do use more modeling, but they also focus on major institutional developments. A broader review of locational works is consistent with the pragmatic empiricism label. The history of locational economics supports the claim that institutionalism, far from disappearing, continues to provide fundamental questions and techniques for modern pragmatic empiricism.