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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.

Amidst the turbulent tides of geopolitical uncertainty and pandemic-induced economic disruptions, the information technology industry grapples with alarming attrition and aggravating talent gaps, spurring a surge in demand for specialized digital proficiencies. Leveraging this imperative, firms seek to attract and retain top-tier talent through generous compensation packages. This study introduces a holistic, integrated theoretical framework integrating machine learning models to develop a compensation model, interrogating the multifaceted factors that shape pay determination.

Drawing upon a stratified sample of 2488 observations, this study determines whether compensation can be accurately predicted via constructs derived from the integrated theoretical framework, employing various cutting-edge machine learning models. This study culminates in discovering a random forest model, exhibiting 99.6% accuracy and 0.08° mean absolute error, following a series of comprehensive robustness checks.

The empirical findings of this study have revealed critical determinants of compensation, including but not limited to experience level, educational background, and specialized skill-set. The research also elucidates that gender does not play a role in pay disparity, while company size and type hold no consequential sway over individual compensation determination.

The research underscores the importance of equitable compensation to foster technological innovation and encourage the retention of top talent, emphasizing the significance of human capital. Furthermore, the model presented in this study empowers individuals to negotiate their compensation more effectively and supports enterprises in crafting targeted compensation strategies, thereby facilitating sustainable economic growth and helping to attain various Sustainable Development Goals.

The cardinal contribution of this research lies in the inception of an inclusive theoretical framework that persuasively explicates the intricacies of a machine learning-driven remuneration model, ennobled by the synthesis of diverse management theories to capture the complexity of compensation determination. However, the generalizability of the findings to other sectors is constrained as this study is exclusively limited to the IT sector.

Crowdfunding (CF) is a digital-financial innovation that, bypassing credit crisis, bank system rigidities and constraints of the capital market, is allowing new ventures and established companies to get the needed funds to support innovations. After one decade of research, mainly focused on relations between variables and outcomes of the CF campaign, the literature shows methodological lacks about the study of its overall behavior. These reflect into a weak theoretical understanding and inconsistent managerial guidance, leading to a 27% success ratio of campaigns. To bridge this gap, this paper embraces a “complex system” perspective of the CF campaign, able to explore the system's behavior of a campaign over time, in light of its causal loop structure.

By adopting and following the document model building (DMB) methodology, a set of 26 variables and mutual causal relations modeled the system “Crowdfunding campaign” and a data set based on them and crafted to model the “Crowdfunding campaign” with a causal loop diagram. Finally, system archetypes have been used to link the causal loop structure with qualitative trends of CF's behavior (i.e. the raised capital over time).

The research brought to 26 variables making the system a “Crowdfunding campaign.” The variables influence each other, thus showing a set of feedback loops, whose structure determines the behavior of the CF campaign. The causal loop structure is traced back to three system archetypes, presiding the behavior in three stages of the campaign.

The value of this paper is both methodological and theoretical. First, the DMB methodology has been expanded and reinforced concerning previous applications; second, we carried out a causation analysis, unlike the common correlation analysis; further, we created a theoretical model of a “Crowdfunding Campaign” unlike the common empirical models built on CF platform's data.

Industry 4.0 is a promising concept that allows industries to meet customers’ demands with flexible and resilient processes, and highly personalised products. This concept is made up of different dimensions. For a long time, innovative digital technology has been thought of as the only dimension to succeed in digital transformation projects. Other dimensions have been identified such as organisation, strategy, and human resources as key while rolling out digital technology in factories. From these findings, researchers have designed industry 4.0 theoretical models and then built readiness models that allow for analysing the gap between the company initial situation and the theoretical model. Nevertheless, this purely deductive approach does not take into consideration a company’s background and context, and eventually favours one single digital transformation model. This article aims at analysing four actual digital transformation projects and demonstrating that the digital transformation’s success or failure depends on the combination of two variables related to a company’s background and context. This research is based on a double approach: deductive and inductive. First, a literature review has been carried out to define industry 4.0 concept and its main dimensions and digital transformation success factors, as well as barriers, have been investigated. Second, a qualitative survey has been designed to study in-depth four actual industry digital transformation projects, their genesis as well as their execution, to analyse the key variables in succeeding or failing. 46 semi-structured interviews were carried out with projects’ members; interviews have been analysed with thematic content analysis. Then, each digital transformation project has been modelled regarding the key variables and analysed with regards to succeeding or failing. Investigated projects have consolidated the models of digital transformation. Finally, nine digital transformation types have been identified.

This article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector.

The article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed.

This study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation.

Existing research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dynamic EPC risk allocation of EPC projects. By addressing the contract incompleteness of the risk allocation, an operational method is developed. This is a new approach to allocate risks for EPC projects in a dynamic and staged way.

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.

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 aims to derive and estimate a theory-based empirical specification that models a firm’s choices of its international diversification (ID) and product diversification (PD) and how they evolve over time in response to shocks that alter the relative cost and relative profitability of ID and PD.

We use longitudinal data on U.S. manufacturing firms from 1984 to 1999, a period of intense shocks associated with rapid globalization, to estimate a dynamic panel data Tobit model that permits lags in a firm’s adjustment to its optimal mix of ID and PD over time.

We find strong support for the theoretical framework underlying our empirical specifications and posited dynamics, with full adjustment estimated to require, on average, 1.5 years, a finding with implications for the time spacing of observations in empirical studies of ID and PD to avoid biased inferences. Among the globalization shocks during the time period studied, our results indicate that global competitive pressures and efficiency gains from global supply integration to be the more important factors driving U.S. firms toward greater ID relative to PD. Augmentation of firms’ organizational (managerial) and physical capital resources is also found to be important for supporting an expansion of ID relative to PD. Technological resource augmentation is instead found to favor expansion of PD relative to ID.

Our empirical specification is novel. It readily incorporates an often ignored but necessary theoretical condition that defines a firm’s optimal choices of its ID and PD, and it allows observed choices at a point in time to deviate from their optimal values.

Mobile e-commerce has brought convenience to consumers. But for goods such as shoes, mobile e-commerce has failed to provide the same experience as consumers would have in physical stores, and that also causes problems for online merchants, such as high return rates. As a result, the augmented reality (AR) virtual shoe-try-on function appeared. The way that AR virtual shoe-try-on study different from other AR virtual try-on studies is that AR virtual shoe-try-on study only satisfies consumers' visual experience and consumers cannot judge whether the shoes are comfort or not. Whether consumers would accept AR virtual try-on function to help them make purchase decision due to the visual experience provided by AR virtual try-on function is worth discussion. Measuring users' perceptions and preferences can help companies design AR shoe-trying functions and provide services more cost-effectively.

To promote the continuous use and better development of such mobile e-commerce based on the technology acceptance model (TAM), this study explored the influencing factors for users' intentions to continue using the AR virtual shoe-try-on function, including the perceived usefulness, perceived ease of use, system quality, perceived playfulness and attitude.

The results of this study showed that TAM is a powerful theoretical tool of the new technology in mobile e-commerce and that the system quality and perceived playfulness also have a positive impact on the original variables of TAM. System quality and perceived playfulness are important predictors of users' continuance intentions to use the AR virtual shoe-try-on function.

The main contribution of this study to model iteration and theoretical update is to verify the applicability of the TAM in the AR shoe-try-on function and to expand TAM model with system quality and perceived playfulness. The authors' results will help shoe enterprises win users' recognition through AR shoe-try-on function and improve users' continuance intention of use.