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The purpose of this paper is to examine whether firms manage the total value of stock option grants downward after the implementation of Statement of Financial Accounting Standards (SFAS) 123R to reduce their reported option expenses.

All Standard & Poor’s (S&P) 1500 firms with available stock option data in 2004 and 2006 are included in the analysis. The authors analyze if the total value of options granted, the per share fair value of options granted, the number of options granted as well as each individual input assumption have changed from the pre-SFAS 123R (i.e. 2004) to the post-SFAS 123R (i.e. 2006) period. We compare post-SFAS123R option pricing assumptions and per share fair value of options granted with their respective expected values to verify the results. We also analyze whether SFAS 123R has differential effects on firms which chose to disclose option expense only in footnotes (“disclosing firms”) versus firms which voluntarily recognized option expense (“recognizing firms”) prior to SFAS 123R.

The results show that after SFAS 123R, the total fair value of stock options granted for disclosing firms declined significantly. The decrease appears to result from managerial discretion over volatility and dividend yield assumptions as well as the reduction in the number of options granted. The evidence suggests that firms engage in not only assumption-based manipulations but also real activities to lower reported stock option expenses. It was also found that disclosing firms lower the total fair value of stock options granted to a greater extent than recognizing firms.

This study adds to prior literature that examines the opportunistic incentives for managers to use discretion in reporting stock option expenses. This study contributes to the earnings management literature by providing another example of manipulating earnings through real activities. Finally, our study should be of interest to regulators and investors.

The use of artificial intelligence (AI) in management research is still nascent and has primarily focused on content analyses of text data. Some method scholars have begun to discuss the potential benefits of far broader applications; however, these discussions have not led yet to a wave of corresponding AI applications by management researchers. This chapter explores the feasibility and the potential value of using AI for a very specific methodological task: the reliable and efficient capturing of higher-level psychological constructs in management research. It introduces the capturing of basic emotions and emotional authenticity of entrepreneurs based on their macro- and microfacial expressions during pitch presentations as an illustrative example of related AI opportunities and challenges. Thus, this chapter provides both motivation and guidance to management scholars for future applications of AI to advance management research.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.

This study aims to examine the effects of character body size, subject body size and product type on female adolescents’ attitudes toward the character. Given prior research showing that adolescents identify more strongly with those whom they view as similar to themselves, it is possible that heavy and obese adolescents will react more favorably to plus-size ad characters.

Two studies were conducted, one with females aged 12–14 and a second with females aged 15–17. Based on their body mass index, subjects were classified as of small/average size or overweight/obese. Ads featured either a thin, average-size or plus-size character, and promoted either a healthful or an unhealthful snack item.

In general, small/average size female adolescents responded more favorably toward thin characters, whereas their overweight/obese counterparts responded more favorably toward plus-size characters. Moreover, subjects’ responses were not moderated by the nutritional value (healthful vs unhealthful) of the product being advertised.

To effectively promote healthy foods to overweight/obese adolescents, it may be advantageous to incorporate plus-size characters. Additional research is needed, however, to determine whether this approach can effectively influence brand attitudes and consumption behaviors.

As obesity rates continue to rise, it has become vitally important to encourage healthier food choices among youth. To develop effective communication strategies, marketers need to better understand how young consumers respond to various advertising cues.

To the best of the authors’ knowledge, this is the first study to examine the effect of character size and subject size on female adolescents’ attitudes toward the character.

The purpose of this paper is to re‐examine the issue of financial analysts' reaction to enterprise resource planning (ERP) announcements by employing actual firm data and archival earnings forecast observations. As an extension of prior ERP studies, this paper also tests whether forecast revisions vary with the timing of adoption.

Based on 188 firms that announced ERP plans during the years 1993 through 2002, this paper investigates the financial analysts' reaction to ERP announcements by comparing their earnings forecasts issued immediately before and after the ERP announcement. To examine the effect of adoption timing, this paper partitions the sample into three groups: early (1993‐1997), middle (1998‐1999) and late (2000‐2002) adopters.

Results show that significantly positive revisions occur in longer term forecasts (i.e. three‐year‐ahead forecasts) but not in the shorter term predictions such as one‐ and two‐year‐ahead forecasts. In addition, there is some weak evidence that financial analysts react less positively to middle adopters than to early or late adopters. This finding could be attributed to the fact that many firms adopted ERP systems to work out the Y2K problems during the 1998‐1999 period.

The main finding confirms that financial analysts consider ERP implementations beneficial to the adopters in the long term. Companies contemplating ERP adoption should take this time horizon into account.

We propose a local linear functional coefficient estimator that admits a mix of discrete and continuous data for stationary time series. Under weak conditions our estimator is asymptotically normally distributed. A small set of simulation studies is carried out to illustrate the finite sample performance of our estimator. As an application, we estimate a wage determination function that explicitly allows the return to education to depend on other variables. We find evidence of the complex interacting patterns among the regressors in the wage equation, such as increasing returns to education when experience is very low, high return to education for workers with several years of experience, and diminishing returns to education when experience is high. Compared with the commonly used parametric and semiparametric methods, our estimator performs better in both goodness-of-fit and in yielding economically interesting interpretation.

The purpose of this paper is twofold: to identify, evaluate and structure the research that focusses on omni-channel retailing from the perspective of logistics and supply chain management; and to reveal the intellectual foundation of omni-channel retailing research.

The paper applies a multi-method approach by conducting a content-analysis-based literature review of 70 academic papers. Based on the reference lists of these papers, the authors performed a citation and co-citation analysis based on the 34 most frequently cited papers. This analysis included multidimensional scaling, a cluster analysis and factor analysis.

The study reveals the limited consideration of logistics and supply chain management literature in the foundation of the omni-channel retailing research. Further, the authors see a dominance of empirical research as compared to conceptual and analytical research. Overall, there is a focus on the Western retail context in this research field. The intellectual foundation is embedded in the marketing discipline and can be characterised as lacking a robust theoretical foundation.

The contribution of this research is identifying, evaluating and structuring the literature of omni-channel research and providing an overview of the state of the art of this research area considering its interdisciplinary nature. This paper thus supports researchers looking to holistically comprehend, prioritise and use the underpinning literature central to the phenomena of omni-channel retailing. For practitioners and academics alike, the findings can trigger and support future research and an evolving understanding of omni-channel retailing.

The purpose of this paper is to indicate the existence of certain time series characteristics in daily stock returns of four small Asian (Pacific basin) financial markets. It aims to study efficient capital markets (efficient markets hypothesis (EMH)) as results may infer that there are predictable properties of the time series of prices of traded securities on organized markets in Singapore, Malaysia, Korea and Indonesia.

The paper analyses daily variations in financial market data obtained from the Sandra Ann Morsilli Pacific‐basin Capital Markets Research Center (PACAP).

The weak form efficiency test example examines the wide range of trading rules available to common investors. Some theorists try to convince everyone that the weak form of EMH is acceptable due to the weight of academic opinion. The paper finds that for short‐term (daily) changes, the markets of four of the smaller Pacific‐basin stock markets have predictable properties, which leads to the conclusion that the weak‐form EMH does not hold for these markets.

The study is limited to those firms and exchanges studied and the time period covered.

There have been all too few studies of these small financial markets up to now and there is no other study utilizing these data on the Pacific basin (Asia). The results are unique and original.

Supply chain risk management assumes importance in the wake of organizations understanding that their risk susceptibility is dependent on other constituents of their supply chain. The purpose of this paper is to present an approach to effective supply chain risk mitigation by understanding the dynamics between various enablers that help to mitigate risk in a supply chain.

Using interpretive structural modeling the research presents a hierarchy‐based model and the mutual relationships among the enablers of risk mitigation.

The research shows that there exists a group of enablers having a high driving power and low dependence requiring maximum attention and of strategic importance while another group consists of those variables which have high dependence and are the resultant actions.

This classification provides a useful tool to supply chain managers to differentiate between independent and dependent variables and their mutual relationships which would help them to focus on those key variables that are most important for effective risk minimization in a supply chain.

Presentation of enablers in a hierarchy and the classification into driver and dependent categories is unique effort in the area of supply chain risk management.

The purpose of this paper is to explore the knowledge infrastructure of methodological research on partial least squares structural equation modeling (PLS-SEM) from a network point of view. The analysis involves the structures of authors, institutions, countries and co-citation networks, and discloses trending developments in the field.

Based on bibliometric data downloaded from the Web of Science, the authors apply various social network analysis (SNA) and visualization tools to examine the structure of knowledge networks of the PLS-SEM domain. Specifically, the authors investigate the PLS-SEM knowledge network by analyzing 84 methodological studies published in 39 journals by 145 authors from 106 institutions.

The analysis reveals that specific authors dominate the network, whereas most authors work in isolated groups, loosely connected to the network’s focal authors. Besides presenting the results of a country level analysis, the research also identifies journals that play a key role in disseminating knowledge in the network. Finally, a burst detection analysis indicates that method comparisons and extensions, for example, to estimate common factor model data or to leverage PLS-SEM’s predictive capabilities, feature prominently in recent research.

Addressing the limitations of prior systematic literature reviews on the PLS-SEM method, this is the first study to apply SNA to reveal the interrelated structures and properties of PLS-SEM’s research domain.