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The study tests the hypothesis that following the arrival of news in the forex market, the trader/dealers demonstrate two kinds of biases which makes markets volatile: “Recurrence bias,” the belief that news which formerly led to volatility, will again generate volatility (i.e. volatility is recurring), and “Volatility Perception Bias,” the belief that increased volatility following the arrival of a news would persist.

The author uses a preliminary survey and three simulated trading game experiments involving professional foreign exchange dealers to understand these heuristic-led biases and the biases' impact on market volatility.

The paper finds evidence supporting the presence of both “Recurrence Bias” and “Volatility Perception Bias” and a statistically significant, positive impact of participant biases' on market heterogeneity.

The paper makes two important contributions: first, the use of simulated trading game experiment involving professional dealers and second, the incorporation of dealers' biases and heuristics in understanding forex volatility.

Managers can improve the likelihood of new product success with simulated test market (STM) research and software programs. It's a way of getting the had news in the laboratory, without spending millions of dollars only to be hammered in test market cities.

In this paper, the authors tested if promoting the workforce's happiness (through high performance work policies or HPWP) and well-being in European Public companies relates to their profitability (return on equity, ROE), market risk (beta) and stock price return. Also, the authors tested if investors have a performance benefit if they buy a portfolio screened with companies with HPWP.

The authors proxied the quality of the HPWP efforts in the first method with the Refinitiv workforce score. They used this data in an unbalanced panel of eastern, western, northern and southern Europe companies from 2011 to 2022. The panel data also included the ROE, the market risk (beta) and the stock price return of these companies. The authors estimated the corresponding regressions with the panel data and tested the relationship between the workforce score and these three variables. In a second method, they simulated the weekly performance of a portfolio that invested only in European companies with high standards in their HPWP and compared its performance against a conventional market portfolio (with no HPWP screening).

In the first method, the authors found no significant relationship between the workforce score and the ROE, beta, or stock price return in the panel regression, controlling for random effects. In the second one, they found no over or underperformance in the HPWP portfolio against the European market one in the second method.

The results suggest that there is no risk or cost for European Public companies and investors alike if they promote, with better HPWP, the happiness and well-being of their workforce. The findings suggest that if European companies promote HPWP, there will be no adverse impact on their profits, market risk, or stock price performance. Also, investors will not lose performance (against a conventional market portfolio) if they screen their portfolios with this type of workforce-friendly companies.

Increase the scarce literature on the test of the workforce score with company profitability (ROE), stock market price variation and stock market risk level.

An agent-based market simulation is utilized to examine the impact of high frequency trading (HFT) on various aspects of the stock market. This study aims to provide a baseline understanding of the effect of HFT on markets by using a paradigm of zero-intelligence traders and examining the resulting structural changes.

A continuous double auction setting with zero-intelligence traders is used by adapting the model of Gode and Sunder (1993) to include algorithmic high frequency (HF) traders who retrade by marking up their shares by a fixed percentage. The simulation examines the effects of two independent factors, the number of HF traders and their markup percentage, on several dependent variables, principally volume, market efficiency, trader surplus and volatility. Results of the simulations are tested with two-way ANOVA and Tukey’s post hoc tests.

In the simulation results, trading volume, efficiency and total surplus vary directly with the number of traders employing HFT. Results also reveal that market volatility increased with the number of HF traders.

Increases in volume, efficiency and total surplus represent market improvements due to the trading activities of HF traders. However, the increase in volatility is worrisome, and some of the surplus increase appears to come at the expense of long-term-oriented investors. However, the relatively recent development of HFT and dearth of appropriate data make direct calibration of any model difficult.

The simulation study focuses on the structural impact of HF traders on several aspects of the simulated market, with the effects isolated from other noise and problems with empirical data. A baseline for comparison and suggestions for future research are established.

The world's leading manufacturers of fast moving consumer goods (FMCGs) generate up to 50 per cent of their revenues in emerging markets. Simulated test marketing (STM) is a common practice deployed by these companies to forecast new product sales. Emerging markets represent only a small portion of the global STM business. The purpose of this paper is to incorporate and further explore some key trends anticipated in the development of the future generation of STM models by drawing specific attention to the issues currently experienced in one of the emerging markets, Russia.

A quantitative survey of Russian client-side marketing experts provides strong evidence for the need to further improve and modify STM methods, addressing new challenges in rapidly developing markets of Eastern Europe, the Middle East, Asia, Latin America and Africa.

Marketers in Russia believe many STM approaches poorly reflect the nuances and characteristics of their markets. This has implications for global players targeting emerging markets based on assumptions formed for STM in their home markets.

This is a preliminary study which warrants following up. Its basis in Russia arguably has implications for other emerging markets, but whether these findings are evident in other markets needs to be tested.

FMCG companies in Russia would appreciate a flexible, proactive, “client-oriented” approach as opposed to conservative, “model-centered” services based on “global” execution standards. This would lead to the co-creation of STM models that could achieve more accurate forecasts in emerging markets and achieve a greater level of confidence in the use of STM among multinational FMCG companies.

The research undertaken leads to a general conclusion that although traditional STM models have attained relatively high awareness among FMCGs in Russia, their use is still limited as there is a perception of this being a research instrument that would need adaptation to the Russian market. Instead, simpler, cheaper and less time consuming alternatives are often employed, such as expert assessments, basic quantitative or qualitative tests. Although the most commonly acknowledged advantages of STM are well understood in Russia, there are some key barriers to its widespread adoption: poor quality or insufficient market data, lack of local market experience and validations, lower forecast accuracy as compared to “western” markets, low flexibility in terms of design and cost.

The purpose of this study was to investigate whether the layout has an effect on cross-contaminations levels at farmers markets.

We used social cognitive theory's triadic reciprocity model to investigate how influencing the environment could change the behaviors of farmers’ market consumers and reduce the risk of microbial cross-contamination using a Fluorescent Compound (FC). For this purpose, a 3 × 2 experimental between-subject factorial design was utilized in this study: three farmers market layouts (i.e. U-shaped [U-S], L-shaped [L-S] and square-shaped [S–S]) and two different set-ups per market (i.e. produce and non-produce vendors completely separated, and alternating produce and non-produce vendors). FC was utilized to simulate microbial contamination on the participants (n = 54) hands. The participants were allowed to walk through the layout for 3 min and touch items after which a total of 475 swab samples were processed and recorded for absorbance levels.

The results indicated that the cross-contamination level of the U-S market was significantly lower (p < 0.001) than those of the L-S and S–S markets. The best market layout and set-up based on the average levels of simulated cross-contamination were the U-S market, particularly with the A set-up, where produce and non-produce booths were scattered.

This study is the first to use the quantification of FC to identify the impact of a farmers’ market layout/design on cross-contamination levels. These results can be used to provide guidance to market managers on layout and design from a safety standpoint to reduce the risk of cross-contamination.

The purpose of this research is to determine how managers’ decisions to discontinue products may be affected by their cognitive and demographic characteristics. Research in product management and entrepreneurship has primarily focused on the introduction of innovations and the marketing of emerging and existing products in the marketplace. Considerably less research has focused on product elimination and how marketing managers decide to remove poorly performing products from a given product portfolio. Nevertheless, product elimination decisions are critical to maintaining business health and protecting firm profits, and are a commonly encountered decision for entrepreneurs and managers of existing products. This study empirically explores the role of factors that may affect a manager's decisiveness in eliminating poorly performing products from a product portfolio.

Using a simulated business environment, this study empirically explores the role of factors that may affect a manager’s decisiveness in eliminating poorly performing products from a product portfolio. Product portfolio decisions are presented to a sample of emerging managers using a computer simulation, and the impact of manager characteristics, namely, cognitive style, gender, academic profile and entrepreneurial intentions on product elimination decisiveness is examined using regression analysis.

The findings indicate dominant effects for cognitive style and academic profile in driving the decisiveness of product elimination decisions.

The findings highlight the importance of the academic profile and cognitive style of those entrusted with managing product portfolios, especially as is related to product elimination decisions.

The findings imply a need for determining the optimal fit of candidates for product portfolio management roles, based on factors such as cognitive style, academic performance and academic area of specialization.

Given the role of entrepreneurial enterprises in enabling social equity, this research highlights the need for entrepreneurial education focusing not only on product introduction but also product omission.

This research expands prior research findings on innovation, promotion and elimination of products by asking what happens at the end of a product’s life when the prospects for a product are no longer strong. The research shows that some managers are less decisive and therefore may be challenged when handling product portfolios with sub-performing products. The findings indicate cognitive and academic influences on product elimination indecisiveness and open new avenues for further examining similar influences in managerial decision-making. This line of work therefore encourages inquiry into the drivers of the important decision of product elimination.

Data collected from 202 large and 92 small consumer goods manufacturing firms were analysed to examine the perceptions and experiences of these companies with test marketing as part of their new product development strategy. Seventy six per cent of the large companies and twenty four per cent of the small firms in the study test marketed their new products before full‐scale introduction. Chi‐square analysis indicated a relationship between firm size, type of business/industry, the scope of marketing operations, and whether the firm conducted test marketing or not. Cost, time constraints, and the generic nature of the product were the most prominent reasons cited by all firms for not conducting test marketing. In addition, small firms cited their size as amajor reason they did not engage in test marketing.

Structural models of demand founded on the classic work of Berry, Levinsohn, and Pakes (1995) link variation in aggregate market shares for a product to the influence of product attributes on heterogeneous consumer tastes. We consider implementing these models in settings with complicated products where consumer preferences for product attributes are sparse, that is, where a small proportion of a high-dimensional product characteristics influence consumer tastes. We propose a multistep estimator to efficiently perform uniform inference. Our estimator employs a penalized pre-estimation model specification stage to consistently estimate nonlinear features of the BLP model. We then perform selection via a Triple-LASSO for explanatory controls, treatment selection controls, and instrument selection. After selecting variables, we use an unpenalized GMM estimator for inference. Monte Carlo simulations verify the performance of these estimators.

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.