Search results

The purpose of this research is to develop a comprehensive model to better understand competitive dynamics between mobile payment providers in a multi-sided market featuring customers and merchants. This is undertaken by modeling customers performing financial transactions with merchants while two mobile payment systems (MPS) providers deploy different strategies to compete for market share.

The authors developed an agent-based simulation model using the NetLogo environment. The simulation featured two competing platform providers, 1,000 customer agents and 50 merchant agents. Past research, interviews and surveys were conducted to accurately model the behavior of the agents. Each simulation run lasted for 50 time periods. A total of 1,024 experimental conditions were designed to model different competitive environments, and 50 replications were conducted for a total of 51,200 experiments.

The simulation model provides insight into MPS platform providers’ competitive strategies by simultaneously modelling socioeconomic interactions between customers, merchants and MPS.

From a methodological perspective, the paper contributes a comprehensive model that can be used to study competitive dynamics between competing platforms in a multi-sided market. From the perspective of competitive strategies, the results show that pricing alone is not sufficient to influence MPS diffusion. Interactions between pricing, customers’ risk perception, perceived security and ease of use of the platform create unexpected same-side and cross-side network effects, which affect MPS diffusion.

While pricing remains a crucial lever for MPS to compete for market share, they should focus on enhancing customers’ and merchants’ trust and reduce their risk perception. This can be done through the improvement of the user experience of their platform, development of educational materials and marketing campaigns that address concerns around security, data breaches and perceived risk.

The paper is a direct response to a recent call for action on studying competition between MPS platforms by simultaneously modelling the socio-economic behavior of heterogeneous consumers and merchants. The proposed agent-based simulation model can be used to provide insights into competitive strategies and as a building block for subsequent research in this area.

This study aims to investigate the impact of ex-ante regulatory interventions on emerging digital markets related to data sharing and combination practices. Specifically, it evaluates how such interventions influence market contestability by considering data network effects and the economic value of data.

The research uses agent-based modeling and simulations to analyze the dynamics of value generation and market competition related to the regulatory obligations on data sharing and combination practices.

Results show that while the promotion of data sharing through data portability and interoperability has a positive impact on the market, restricting data combination may damage value generation or, at best, have no positive impact even when it is imposed only on those platforms with very large market shares. More generally, the results emphasize the role of regulators in enabling the market through interoperability and service multihoming. Data sharing through portability fosters competition, while the usage of complementary data enhances platform value without necessarily harming the market. Service provider multihoming complements these efforts.

Although agent-based modeling and simulations describe the dynamics of data markets and platform competition, they do not provide accurate forecasts of possible market outcomes.

This paper presents a novel approach to understanding the dynamics of data value generation and the effects of related regulatory interventions. In the absence of real-world data, agent-based modeling provides a means to understand the general dynamics of data markets under different regulatory decisions that have yet to be implemented. This analysis is timely given the emergence of regulatory concerns on how to stimulate a competitive digital market and a shift toward ex-ante regulation, such as the regulatory obligations to large gatekeepers set in the Digital Markets Act.

This study explores the emergence of discrimination based on observable characteristics. In many instances, agents presume differences arising from traits such as race or gender, even when these parameters are irrelevant to the situation at hand. This paper intends to reveal an emergent behavior and a persistent culture of discrimination caused by miscategorization in strategic interactions.

We assume that agents occasionally engage in conflicts modeled as asymmetric hawk and dove games, where boundedly rational agents may categorize their opponents based on observable traits to make effective decisions. Three categorization strategies are considered: fine-grained, regular, and coarse-grained. Subsequently, an evolutionary agent-based model is employed to examine the performance of these strategies in a dynamic environment.

The results demonstrate that fine-grained categorization provides an advantage when the cost of fighting is low, while coarse-grained categorizers exhibit more peaceful behavior, gaining an advantage when the cost of conflict is high. Our primary finding indicates the emergence of discrimination based on non-relevant traits, manifested through consistent aggressive behavior towards individuals possessing these traits.

This paper is the first to investigate the emergence of discrimination without assuming prior differences between groups. Previous studies have assumed either an initial population difference or a homophily-based approach. In contrast, we demonstrate that discrimination can emerge even in the absence of such assumptions. Discrimination between two groups may arise as long as there are agents who label these categories.

Modern subway transportation systems need to satisfy increasing safety demands to rapidly evacuate passengers under hazardous emergency circumstances, such as fires, accidents or terrorist attacks, to reduce passenger injuries or life losses. The emergency evacuation capacity (EEC) of a subway station needs to be revised timely, in case passenger demand increases or the evacuation route changes in the future. However, traditional ways of estimating EEC, e.g. fire drills are time- and resource-consuming and are difficult to revise from time to time. The purpose of this study is to establish an intuitive modelling approach to increase the EEC of subway stations in a stepwised manner.

This study develops an approach to combine agent-based evacuation modelling and building information modelling (BIM) technology to estimate the total evacuation time of a subway station.

Evacuation time can be saved (33% in the studied case) from iterative improvements including stopping escalators running against the evacuation flow and modifying the geometry around escalator exits. Such iterative improvements rely on integrating agent-based modelling and BIM.

The agent-based model can provide a more realistic simulation of intelligent individual movements under emergency circumstances and provides precise feedback on locations of evacuation bottlenecks. This study also examined the effectiveness of two rounds of stepwise improvements in terms of operation or design to increase the EEC of the station.

The purpose of this paper is to examine the impact of affiliate marketing strategies as a tool for increasing customers' engagement and vulnerability over financial services. This is attempted by examining the connection between affiliate marketing factors and customers' brand engagement and vulnerability metrics.

The authors developed a three-staged methodological context, based on the 7 most known centralized payment network (CPN) firms' website analytical data, which begins with linear regression analysis, followed by hybrid modeling (agent-based and dynamic models), so as to simulate brand engagement and vulnerability factors' variation in a 180-day period. The deployed context ends by applying the cognitive modeling method of producing heatmaps and facial analysis of CPN websites to the selected 47 vulnerable website customers, for gathering more insights into their brand engagement.

Throughout the simulation results of the study, it becomes clear that a higher number of backlinks and referral domains tend to increase CPN firms' brand-engaged and vulnerable customers.

From the simulation modeling process, the implication for backlinks and referral domains as factors that enhance website customers' brand engagement and vulnerability has been highlighted. A higher number of brand-engaged website customers could mean that vulnerable categories of customers would be impacted by CPNs' affiliate marketing. Improving those customers' knowledge of the financial services utility is of utmost importance.

The outcomes of the research indicate that online banking service providers can increase their customers' engagement with their brands by adopting affiliate marketing techniques. To avoid the increase in customers' vulnerability, marketers should aim to apply affiliate marketing strategies to domains relevant to the provided financial services.

The paper's outcomes provide a new approach to the literature, where the website customer's brand engagement comes out as a valuable metric for estimating online banking sector customers' vulnerability.

This study focuses on (re-)introducing computer simulation as a part of the research paradigm. Simulation is a widely applied research method in supply chain and operations management. However, leading journals, such as the International Journal of Operations and Production Management, have often been reluctant to accept simulation studies. This study provides guidelines on how to conduct simulation research that advances theory, is relevant, and matters.

This study pooled the viewpoints of the editorial team of the International Journal of Operations and Production Management and authors of simulation studies. The authors debated their views and outlined why simulation is important and what a compelling simulation should look like.

There is an increasing importance of considering uncertainty, an increasing interest in dynamic phenomena, such as the transient response(s) to disruptions, and an increasing need to consider complementary outcomes, such as sustainability, which many researchers believe can be tackled by big data and modern analytical tools. But building, elaborating, and testing theory by purposeful experimentation is the strength of computer simulation. The authors therefore argue that simulation should play an important role in supply chain and operations management research, but for this, it also has to evolve away from simply generating and analyzing data. Four types of simulation research with much promise are outlined: empirical grounded simulation, simulation that establishes causality, simulation that supplements machine learning, artificial intelligence and analytics and simulation for sensitive environments.

This study identifies reasons why simulation is important for understanding and responding to today's business and societal challenges, it provides some guidance on how to design good simulation studies in this context and it links simulation to empirical research and theory going beyond multimethod studies.

This research seeks to explore the intersection between modularity and conformity in organizational contexts. Modularity, a cornerstone of organizational design, pertains to the decomposability of tasks within an organization into subtasks with internal interdependence and external independence. Conformity, on the other hand, is the adjustment of an individual’s behavior to match that of others, often driven by a desire to adhere to social norms.

We employ agent-based modeling and simulation as a technique to model organizations as complex systems. This approach allows us to delve into the effects of modularity in organizational structures on organizational performance, with a particular emphasis on the role of conformity in this relationship. We treat conformity as exogenously given, which allows us to focus on its effects rather than its emergence.

The results demonstrate that a concentration of interdependent tasks within fewer departments can boost overall performance. Conformity decreases performance in all organizational structures except for cases when the departments work on highly similar tasks. This decline in performance can also explain why functional organizational structures are still being used in practice even though they are less modular than divisional structures — they feature lower levels of conformity and, thus, face smaller decline. Finally, we find that in highly complex settings, organizational performance can, surprisingly, be improved as complexity within departments increases.

To the best of our knowledge, this study is the first to explore the modularity in organizational structures in presence of conformity. Distinctively, we adapt the NKCS model from evolutionary biology to our study, and perform an exhaustive analysis by examining all possible combinations of parameters that refer to the task allocation within organizations. We thereby contribute a unique perspective to the discourse on organizational theory and behavior.

This study aims to specify whether heterogeneous reference-point-based aspirations are related to the cooperation levels of R&D alliance portfolios in a positive or negative (or nonlinear) way, and to unveil how cooperative behaviors evolve in recurrent project cooperation.

This study establishes a network containing a cooperation subnetwork and a project subnetwork based on patent data in the “deep learning” field to investigate how cooperative behaviors evolve in R&D alliance portfolios. A model of evolutionary games on complex networks is constructed to gain insight into the dynamic evolution of DMs’ strategies.

First, the heterogeneous aspirations of DMs can improve the cooperation level in R&D alliance portfolios. Second, compared to prudent DMs, aggressive DMs are more likely to choose the cooperation strategy, implying that an appropriate aspiration level nurtures cooperative R&D endeavors with partners. Third, the effects of effort complementarity, knowledge reorganization capabilities and cooperation supervision on cooperation are contingent on the distribution of DMs’ aspiration types.

Policymakers should identify aspiration types of DMs when screening partners. They can encourage partners to focus more on historical payoffs and establish relatively higher aspiration levels to improve the cooperation level. Developing highly detailed contracts becomes crucial when cooperating with firms that possess extensive knowledge reorganization capabilities.

This work contributes a theoretical framework for investigating cooperation in R&D alliance portfolios through the lens of evolutionary games on complex networks, thus revealing the effects of heterogeneous reference-point-based aspirations of DMs on R&D cooperation.

Digital platforms that offer on-demand gig work, while providing work opportunities in the economy, have raised social and economic concerns. Though extensive research on regulation of the gig economy exists, the effect of economic regulations on the welfare of the workers is not well understood. In this work, this paper aims to specifically study the effect of minimum wage and leisure regulations on the unemployment rate and offered wages. This paper also analyses the effect of up-skilling of the workers on unemployment and wages.

This paper builds an agent-based model of the labour market with heterogeneous workers and online platform firms that interact to match supply and demand. This paper also interviewed online workers in the two under-studied markets in online beauty and house maintenance services in India and included salient observations in to the model. This paper further validates the model findings with the interview observations.

Extensive simulations of the model indicate that the regulator's intervention on minimum wage and leisure reduces unemployment and offers better wages/leisure in the short term. However, these cannot be sustained unless the workers upgrade their skills, thereby improving their value to the employers. This paper also corroborates the authors’ interview observations on platforms deviating on stated worker contracts by simulating the same in the model. This paper finds that when platforms deviate on their stated incentive schemes, the unemployment rate tends to increase. This paper also finds that the emergence of online platforms in an erstwhile off-line market decreases the average unemployment rate with a moderate increase in the offered wage and leisure.

In this work, the focus has been to determine the worker-platform dyadic relationship. However, this is affected by consumer-related attributes such as ratings and associated reputation systems to promote trust between different stakeholders. Examining such a triadic relationship between consumers, platform and workers is required to comprehensively address the challenges of online gig economy.

Skilling and training are critical for worker mobility across tasks and jobs, especially in the gig economy. Gig workers, in general, seek to improve their skill level through self- or platform-enabled training programmes. The workers are able to generate more revenue through the new skills and hence can improve their reservation wage as well. This in turn increases average offered wages and reduces the overall unemployment in the sector. Despite attempts to classify gig workers as formal workers by labour laws across countries, there is resistance from online platforms. This is due to increased liability and responsibility that the platforms have to incur that possible increase their costs and expenses. This study shows that regulations, such as minimum leisure or minimum wage, increase the average wage or leisure in the market and increase unemployment. However, this might be a short-term phenomenon. In the long term, the gig workers benefit by enhancing their skills to not only stay employed but also bargain for better wages and leisure. The governments can play a larger role by facilitating upskilling programmes for the gig workers.

An extensive literature survey indicates that while most of the work on gig economy regulation emphasises the social and legal aspects, this work is unique in modeling the techno-economics of gig work. Further, while most of the economic research on gig work, focuses on consumers, this work focuses on the under-researched area of worker welfare. This paper also validates the model results with findings from the interviews with gig workers.

Many economic, political and socio-cultural events in the 2020s have been strong headwinds for architecture, engineering and construction (AEC). Nevertheless, technological advancements (e.g. artificial intelligence (AI), big data and robotics) provide promising avenues for the development of AEC. This study aims to map the state of the literature on automation in AEC and thereby be of value not only to those researching automation and its composition of a variety of distinct technological and system classes within AEC, but also to practitioners and policymakers in shaping the future of AEC.

This review adopts scientometric methods, which have been effective in the research of large intra and interdisciplinary domains in the past decades. The full dataset consists of 1,871 articles on automation in AEC.

This overarching scientometric review offers three interdisciplinary streams of research: technological frontiers, project monitoring and applied research in AEC. To support the scientometric analysis, the authors offer a critical integrative review of the literature to proffer a multilevel, multistage framework of automation in AEC, which demonstrates an abundance of technological paradigm discussions and the inherent need for a holistic managerial approach to automation in AEC.

The authors underline employee well-being, business sustainability and social growth outcomes of automation and provide several managerial implications, such as the strategic management approach, ethical management view and human resource management perspective. In doing so, the authors seek to respond to the Sustainable Development Goals proposed by the United Nations as this becomes more prevalent for the industry and all levels of society in general.