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1 – 10 of over 3000Joonwook Park, Priyali Rajagopal, William Dillon, Seoil Chaiy and Wayne DeSarbo
Joint space multidimensional scaling (MDS) maps are often utilized for positioning analyses and are estimated with survey data of consumer preferences, choices, considerations…
Abstract
Purpose
Joint space multidimensional scaling (MDS) maps are often utilized for positioning analyses and are estimated with survey data of consumer preferences, choices, considerations, intentions, etc. so as to provide a parsimonious spatial depiction of the competitive landscape. However, little attention has been given to the possibility that consumers may display heterogeneity in their information usage (Bettman et al., 1998) and the possible impact this may have on the corresponding estimated joint space maps. This paper aims to address this important issue and proposes a new Bayesian multidimensional unfolding model for the analysis of two or three-way dominance (e.g. preference) data. The authors’ new MDS model explicitly accommodates dimension selection and preference heterogeneity simultaneously in a unified framework.
Design/methodology/approach
This manuscript introduces a new Bayesian hierarchical spatial MDS model with accompanying Markov chain Monte Carlo algorithm for estimation that explicitly places constraints on a set of scale parameters in such a way as to model a consumer using or not using each latent dimension in forming his/her preferences while at the same time permitting consumers to differentially weigh each utilized latent dimension. In this manner, both preference heterogeneity and dimensionality selection heterogeneity are modeled simultaneously.
Findings
The superiority of this model over existing spatial models is demonstrated in both the case of simulated data, where the structure of the data is known in advance, as well as in an empirical application/illustration relating to the positioning of digital cameras. In the empirical application/illustration, the policy implications of accounting for the presence of dimensionality selection heterogeneity is shown to be derived from the Bayesian spatial analyses conducted. The results demonstrate that a model that incorporates dimensionality selection heterogeneity outperforms models that cannot recognize that consumers may be selective in the product information that they choose to process. Such results also show that a marketing manager may encounter biased parameter estimates and distorted market structures if he/she ignores such dimensionality selection heterogeneity.
Research limitations/implications
The proposed Bayesian spatial model provides information regarding how individual consumers utilize each dimension and how the relationship with behavioral variables can help marketers understand the underlying reasons for selective dimensional usage. Further, the proposed approach helps a marketing manager to identify major dimension(s) that could maximize the effect of a change of brand positioning, and thus identify potential opportunities/threats that existing MDS methods cannot provides.
Originality/value
To date, no existent spatial model utilized for brand positioning can accommodate the various forms of heterogeneity exhibited by real consumers mentioned above. The end result can be very inaccurate and biased portrayals of competitive market structure whose strategy implications may be wrong and non-optimal. Given the role of such spatial models in the classical segmentation-targeting-positioning paradigm which forms the basis of all marketing strategy, the value of such research can be dramatic in many marketing applications, as illustrated in the manuscript via analyses of both synthetic and actual data.
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Anup Menon Nandialath, Emily David, Diya Das and Ramesh Mohan
Much of what we learn from empirical research is based on a specific empirical model(s) presented in the literature. However, the range of plausible models given the data is…
Abstract
Purpose
Much of what we learn from empirical research is based on a specific empirical model(s) presented in the literature. However, the range of plausible models given the data is potentially larger, thus creating an additional source of uncertainty termed: model uncertainty. The purpose of this paper is to examine the effect of model uncertainty on empirical research in HRM and suggest potential solutions to deal with the same.
Design/methodology/approach
Using a sample of call center employees from India, the authors test the robustness of predictors of intention to leave based on the unfolding model proposed by Harman et.al. (2007). Methodologically, the authors use Bayesian Model Averaging (BMA) to identify the specific variables within the unfolding model that have a robust relationship with turnover intentions after accounting for model uncertainty.
Findings
The findings show that indeed model uncertainty can impact what we learn from empirical studies. More specifically, in the context of the sample, using four plausible model specifications, the authors show that the conclusions can vary depending on which model the authors choose to interpret. Furthermore, using BMA, the authors find that only two variables, job satisfaction and perceived organizational support, are model specification independent robust predictors of intention to leave.
Practical implications
The research has specific implications for the development of HR analytics and informs managers on which are the most robust elements affecting attrition.
Originality/value
While empirical research typically acknowledges and corrects for the presence of sampling uncertainty through p-values, rarely does it acknowledge the presence of model uncertainty (which variables to include in a model). To the best of the authors’ knowledge, it is the first study to show the effect and offer a solution to studying total uncertainty (sampling uncertainty + model uncertainty) on empirical research in HRM. The work should open more doors toward more studies evaluating the robustness of key HRM constructs in explaining important work-related outcomes.
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Ming-min Liu, L.Z. Li and Jun Zhang
The purpose of this paper is to discuss a data interpolation method of curved surfaces from the point of dimension reduction and manifold learning.
Abstract
Purpose
The purpose of this paper is to discuss a data interpolation method of curved surfaces from the point of dimension reduction and manifold learning.
Design/methodology/approach
Instead of transmitting data of curved surfaces in 3D space directly, the method transmits data by unfolding 3D curved surfaces into 2D planes by manifold learning algorithms. The similarity between surface unfolding and manifold learning is discussed. Projection ability of several manifold learning algorithms is investigated to unfold curved surface. The algorithms’ efficiency and their influences on the accuracy of data transmission are investigated by three examples.
Findings
It is found that the data interpolations using manifold learning algorithms LLE, HLLE and LTSA are efficient and accurate.
Originality/value
The method can improve the accuracies of coupling data interpolation and fluid-structure interaction simulation involving curved surfaces.
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Chunjiang Yang, Qinhai Ma and Ling Hu
The purpose of this paper is first, to overview the current research situation on job embeddedness (JE), including the theoretical underpinning of JE, the definition and…
Abstract
Purpose
The purpose of this paper is first, to overview the current research situation on job embeddedness (JE), including the theoretical underpinning of JE, the definition and dimensions of JE, its comparisons with similar constructs, and its global and composite measure; second, to intergrate the unfolding model, JE and image theory to better understand voluntary turnover – and indicate future research directions.
Design/methodology/approach
An extensive literature search covering several separate electronic databases, including ScienceDirect, EBSCO, Kluwer and Emerald, was conducted. Most of the articles can be acquired online from The University of California Riverside. The validity and reliability are compared between global and composite scales. The authors summarized and categorized the findings of current research.
Findings
JE can be differentiated from those similar constructs and measures already in the literature. Almost all of the studies on JE have found that it predicted voluntary turnover better than job attitudes and perceived ease of movement from traditional models of turnover. Along with extended research on it, JE was disaggregated into two major sub‐dimensions, namely, on‐the‐job and off‐the‐job embeddedness, and it has been extended to occupational and career level.
Research limitations/implications
In this paper, the authors use qualitative methods to evaluate the current studies on JE, only. Meta‐analysis, as a reviewing method, should be used in future research on clarifying the relationships between JE and other constructs in organizational behavior.
Originality/value
This research reviews almost all of the studies on JE from 2001 to 2009 and organizes and categorizes them into three kinds: cause, consequence and theoretical extension. The authors also summarize its relationships with other constructs (e.g. turnover, turnover intention, organizational commitment, organizational citizenship behavior) in various settings. Finally, based on discussion, the authors indicate future research directions.
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This study is motivated in part by the fact that the unfolding 2022 bear market, which has reached the −25% drawdown, has not been preceded by the inverted 10Y-3 m spread or an…
Abstract
Purpose
This study is motivated in part by the fact that the unfolding 2022 bear market, which has reached the −25% drawdown, has not been preceded by the inverted 10Y-3 m spread or an inverted near-term forward spread.
Design/methodology/approach
The authors develop a three-factor probit model to predict/explain the deep stock market drawdowns, which the authors define as the drawdowns in excess of 20%.
Findings
The study results show that (1) the rising credit risk predicts a deep drawdown about a year in advance and (2) the monetary policy easing precedes an imminent drawdown below the 20% threshold.
Originality/value
This study three-factor probit model shows adaptability beyond the typical recessionary bear market and predicts/explains the liquidity-based selloffs, like the 2022 and possibly the 1987 deep drawdowns.
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Zengli Mao and Chong Wu
Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the…
Abstract
Purpose
Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.
Design/methodology/approach
The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.
Findings
Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.
Practical implications
The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.
Social implications
If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.
Originality/value
Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.
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The authors review the German voluntary turnover literature and examine how it reflects and extends the overall knowledge of employee turnover. First, the authors describe legal…
Abstract
The authors review the German voluntary turnover literature and examine how it reflects and extends the overall knowledge of employee turnover. First, the authors describe legal, institutional, and cultural influences specific to Germany that may affect voluntary turnover and its relationships with antecedents and outcomes. The authors then explain how research paradigms, which in German turnover research are primarily embedded in sociology and labor economics and to a lesser degree psychology and management, affect the lens by which voluntary turnover is examined. For instance, the variety of research perspectives leads to a variety of research questions, theories, data, and methodological approaches. Using these diverse perspectives, the authors explain how measurement and data quality concerns may hamper the understanding of turnover in cross-country/cross-cultural comparisons. This review further reveals many similarities with US-based turnover research, regarding the theories, methods, and results. The authors also find that turnover levels are, on average, considerably lower in Germany than in Anglo-Saxon labor markets. The authors suggest that the industry structure in Germany, coined by its strong and traditionally organized “Mittelstand” companies, may partly drive these findings. The authors close by identifying several research opportunities, available through advances in technology to improve the matching process, nonstandard work arrangements (such as in the gig economy), and a broader perspective on institutional peculiarities.
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