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Research on the service–profit chain (SPC) provides important insights regarding how organizations attain service excellence. However, this research stream does not shed light on the mechanisms by which service organizations sustain such excellence, despite the struggles of many organizations to do so. Thus, the purpose of this paper is to develop the SPC as a more dynamic system characterized by feedback loops, accumulation processes, and time delays based on the service operations, human resources, and marketing literatures.

The authors posit the feedback loops operate as virtuous cycles, such that increases in customer perceptions of service quality and in profit margins lead to subsequent increases in the quality of the internal working environment, which ultimately reimpacts performance in a positive way, and so on. The authors test the hypotheses using five years of archival data on 417 full-service US hotels. The unique data set combines longitudinal data from multiple functions, including employee assessments regarding their tools, practices, and abilities to serve customers, customer perceptions of service quality, and objective measures of financial performance.

The authors find support for the idea that some organizations provide customers with high-quality service over time by reinvesting in the inputs responsible for generating the initial success, i.e., in various aspects of the internal working environment.

The analysis of 417 hotels from a single firm may influence the extent to which the findings can be generalized.

By expanding the boundaries of previous conceptual and empirical models investigating SPCs, the authors offer a deeper understanding of the cross-functional character of modern operational systems and the complex dynamics that these systems generate.

The purpose of this paper is to study the effect of the 2008 global financial crisis on housing market dynamics in an emerging economy like India using quarterly data (Q4 2008–2009 to Q1 2018–2019). The study explores the extent of linkages between housing prices, monetary policy and financial stability by explaining the nature of the shocks to the housing sector and the degree of impact of those shocks; the possibility of adverse feedback loop which is beyond the natural levels; and the usefulness of explicit and direct role of monetary policy for the housing market stability, which was the loudest demand immediately after the crisis.

The paper follows a three-step methodology: data transformations, a variable selection process “general-to-specific modelling” with the help of OxMetrics 6 Package, and vector autoregressive modelling with the help of EViews 10. F-test was used to describe the short-term relationships between the variables. Impulse response and variance decomposition were used to explain the type of relationship (negative or positive) and the period of the relationships, respectively.

The study finds that the housing sector is sensitive to the monetary policy shocks, whereas the contribution of the housing market shocks to the fluctuations in other market variables is not substantial, though not negligible. As far as the nature of the shocks is concerned, the observed dynamics in the real house prices are diverging from their fundamental levels. The housing market shocks are more or less static; it rules out the chances for a self-reinforcing feedback loop with the existing setup.

The study concludes that the observed dynamics in the real house prices are diverging from their fundamental levels. Given the limitation, the researchers could extend this study by decomposing the part of the risk to the sector contributed by the other drivers, which may be inherent imperfections in housing markets, weak and unreliable wealth effect, and the presence of behavioural biases.

The present study finds countercyclical measures to be more useful for this sector as compared to the forward-looking monetary policy reforms in this sector. The central bank in India should continue to refrain from responding directly to the housing sector fluctuations. Investors can enjoy investing in the housing sector without any fear of the crisis as of now. The effect of speculation is small but not negligible, which enjoins the investors and the policy-makers to remain watchful. Interest rate, money supply and inflation lead (Granger-cause) the housing prices. This information is relevant for spending and investment decisions.

The study feels that banks should avoid using monetary policy to balance the house prices. This will be beneficial both for the economy and the society, as any change in monetary policy to especially curb out surging housing prices may adversely affect the output, and finally, may lead to the deflation. The fear of deflation may cause devastating economic, financial and social effects.

The study contributes to the literature by shedding some new insights about the interrelationship between macroeconomic variables, housing prices and financial stability in the aftermath of the 2008–2009 financial crisis. Such types of studies are absent from emerging markets, particularly from India.

The purpose of this paper is to explore the dynamic nature of behaviour change over time and to gain insights into the effectiveness of social marketing efforts at three different intervention points under three different delay time conditions.

A system dynamics simulation modelling approach was used.

The findings showed that the effectiveness of social marketing interventions at different points of intervention and delay times is dependent on complex dynamic system interactions and feedback loops.

As the dynamic simulation model was an abstraction or simplified representation, it was only useful to gain insights into generalised patterns of behaviour over time.

The paper provided practical guidance to social marketers’ intent on gaining insights into “where to do” and “when to do” social marketing rather than “how to do” social marketing.

The paper provided theoretical and practical insights into the temporal nature of behaviour change and the effectiveness of social marketing interventions in influencing behaviour over time.

The purpose of this paper is to illustrate the appropriateness of system dynamics (SD) methodology as a tool for humanitarian decision makers to understand the effect of their decisions on humanitarian operations. The authors seek to present the initial stages and preliminary findings of a SD model used to analyze a well‐defined subsystem of humanitarian operations, field vehicle fleet management; then build on this example by identifying an additional area of humanitarian operations for future research using the SD methodology.

Case‐based research was combined with SD methodology to examine the appropriateness of this methodology for use in humanitarian operations.

Humanitarian operations are characterized by multiple actors, feedback loops, time pressures, resource constraints and uncertainty. The authors find that SD has the capacity to accurately represent the dynamic complexity of humanitarian operations, and is therefore an appropriate tool to study these systems.

The well‐defined issue of field vehicle fleet management in humanitarian organizations is used to illustrate an application of SD for humanitarian operations. Due to the difficulty in obtaining necessary data to build the SD model, this study uses estimations based on over three years of research into fleet management in the humanitarian sector. The authors then present an example of a broader but less well‐defined subsystem in the humanitarian sector that can be analyzed using SD methodology to the benefit of the overall humanitarian relief operation.

Decision making in humanitarian operations is usually based on intuition and experience which are not always sufficient to fully understand the global impact of these decisions. SD provides humanitarian decision makers with a method to simulate and compare the impact of alternative decisions that would not be possible in real life situations.

This paper examines the appropriateness of SD methodology to help humanitarians improve the effectiveness of their relief and development programs through better‐informed decision making.

This paper presents one of the first attempts to use SD methodology to build a model for humanitarian operations using a well‐defined subsystem, field vehicle fleet management.

The growth of firms is fundamentally based on selfreinforcing feedback loops, one of the most important of which involves cash flow.When profit margin is positive, sales generate cash, which may then be reinvested to finance the operating cash cycle.We analyze simulations of a sustainable growth model of a generic new venture to assess the importance of taxes, and regulatory costs in determining growth.The results suggest that new ventures are particularly vulnerable to public policy effects, since their working capital resource levels are minimal, and they have few options to raise external funds necessary to fuel their initial operating cash cycles.Clearly, this has potential consequences in terms of gaining competitive advantage from experience effects, word of mouth, scale economies, etc. The results of this work suggest that system dynamics models may provide public policy-makers a cost-effective means to meet the spirit of the U.S. Regulatory Flexibility Act

Many models of markets are based on assumptions of rationality, transparency, efficiency, and homogeneity in various combinations. This paper aims to explain why markets routinely and repeatedly make “mistakes” that are inconsistent with these simplifying assumptions.

System dynamics models are used to show how misestimating demand growth, allowing financial discipline to lapse, unrealistic business planning, and misperception of technology trajectories can produce disastrously wrong business decisions. Examples are drawn from airlines, telecommunications, IT, aerospace, energy, and media.

The undesirable outcomes can include vicious cycles of investment and profitability, market bubbles, accelerated commoditization, excessive investment in dead‐end technologies, giving up on a product that becomes a huge success, waiting too long to reinvent legacy companies, and changes in market leadership. Differentiating transient phenomena from the longer term trends, movement away from vertically integrated business models, and effective use of early warning signs avoid these mistakes, or at least limit the damage that they cause.

Decision makers tend to rely on simple mental models which have serious limitations. They become increasingly deficient as problems grow more complex, as the environment changes more rapidly, and as the number of decision makers increases. The amplification and tipping dynamics typical of highly coupled systems, for example, bandwagon, network, and lemming effects, are not anticipated. Behavioural factors that play critical roles in the evolution of markets often are misunderstood or ignored.

The paper illuminates the effects of bounded rationality, imperfect information, and fragmentation of decision making on the behavior of markets. Models which assume, at least implicitly, that decision makers understand the structure of the market and how it produces the dynamics which can be observed or might potentially occur can be dangerous simplifications and seriously misleading.

In this Industry 4.0 era, third-party logistics (3PL) industries face huge cost pressure to deliver their service. With increase in competition among the players, constant mergers and acquisitions (M&A) have been taking place to sustain competitive advantage. Therefore, this study aims to investigate the growth dynamics among the 3PL service providers.

In this research, the system dynamics methodology was applied to the study of the growth of 3PL industry in Singapore. A population growth model incorporating the predator–prey interaction is developed to account for growth through M&As among 3PLs and their interaction phenomenon are modeled through modified Lotka–Volterra method. The two-species system model consisting of small and medium logistics service providers (SMLSPs as the prey) and the lead logistics providers (LLPs as the predator) are gauged according to the firm size.

Results from the baseline model indicates that Singapore’s logistics industry looks very optimistic for SMLSPs for another 6 years from 2018, while the LLP population will achieve a peak at about 12 years from 2018. Further sensitivity analysis through macroeconomic and microeconomic changes reveals increase in trend of M&As. By varying competitive pressures between firms, results indicate that the LLP population experiences a decreasing rate of increasing SMLSP population falls.

The research provides guidance for logistics and supply chain managers to better understand the critical factors that impact and determine competitive dynamics. The paper further recommends managers to build sustainable logistics strategies to retain competitive advantages.

The research contributes to both economic and social dimensions of logistics sustainability of how resilient the industries are during uncertain conditions. Some of the limitations of this research include the geographic coverage of the study region and other methodological aspects. The research value thus helps policymakers for developing strategic policies for sustainable industrial growth.

Previous research in the area of distributor performance proposed different scales, mostly in western, developed country context. These studies also lacked the consideration of dynamic interaction between variables, which determine the distributor’s performance. This paper proposes a composite Distributor Performance Index (DPI) to evaluate distributors’ performance based on at the “Enables” and “Results”, taking a system dynamics approach. The model results have been discussed and validated, in business marketing channel. The context of this study is India, an emerging market.

The paper aims to form system dynamics modeling in introduced in conjunction with econometric analysis and planned scenario analysis which will uniquely structure the process whereby the ex ante capital values of the prime retail real estate sector.

The integrated system dynamics model investigates the structural factors affecting a unique expectation‐centered capital value (CV) formation of the prime retail real estate sector, through system dynamics modeling, econometric analysis , and the analysis of planned scenarios. This model extends beyond the usual lags and time line aspect of the price discovery process. The retail real estate sector is investigated within the Singapore context, as this sector changes dynamically and non‐linearly in relation to rental, cost and general demand expectations and to exogenous shocks like the Severe Advanced Respiratory Syndrome (SARS) outbreak. These macroeconomic factors are introduced to investigate their impact on retail space CVs through sensitivity analysis, during the simulation period of 20 quarters from the zero reference quarter (2Q2002).

The paper finds that simulation runs of the expectations‐centered system dynamics model are based on three scenarios. Sensitivity analysis is conducted for each scenario. Optimistic scenarios' CVs are lower than those of the likely scenario, owing to developers forming excessively high expectations that cannot be met by the actual rental levels. Pessimistic scenarios' CVs are highest. Based on bounded logic and the conditions for all scenarios, there are huge differences in expectations resulting in a large disparity in the endogenous CVs. Low actual rents are primarily due to poor informational efficiency, as the prime retail real estate sector is not transparent enough, and that many transactions are privately closed. Expectations cannot be met as the market information is not disseminated extensively through the agents and players. The scenarios clearly highlight the problem of informational non‐availability in the sector. The main policy implication is a need for a more transparent system of sharing rental and pricing information for the retail real estate sector, which is meaningful for real estate developers, investors and urban planners to sustain the retail real estate sector's viability.

This paper takes system dynamics modeling to the next level of incorporating econometric analysis, to estimate the sensitivity of retail rent to cost and the change in retail rent, for effectively structuring the dynamic process whereby the ex ante CVs of the prime retail sector in Singapore are formed and assessed, through a unique and rigorous expectations‐centered system dynamics model of rents, cost, retail stock, general demand and exogenous factors.

The purpose of this paper is to investigate inter-organizational designs for care–cure conditions in which low-risk patients are cared for in specialized care organizations and high-risk patients are cared for in specialized cure organizations. Performance impacts of increasing levels of integration between these organizations are analyzed.

Mixed methods were used in Dutch perinatal care: analysis of archival data, clinical research and system dynamics simulation modeling.

Inter-organizational design has an effect on inter-organizational dynamics such as collaboration and trust, and also on the operational aspects such as patient flows through the system. Solutions are found in integrating care and cure organizations. However, not all levels of integrated designs perform better than a design based on organizational separation of care and cure.

A clear split between midwifery practices (care) and obstetric departments (cure) will not work since all pregnant women need both care and cure. Having midwifery practices only works well when there are high levels of collaboration and trust with obstetric departments in hospitals. Integrated care designs are likely to exhibit superior performance. However, these designs will have an adverse effect on organizations that are not part of this integration, since integrating only a subset of organizations will feed distrust, low collaboration and hence low performance.

The originality of this research is derived from its multi-method approach. Archival data and clinical research revealed the dynamic relations between organizations. The caveat of some integrated care models was found through simulation.