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This paper has the objective of demonstrating a more structured and useful method for evaluating functionality of enterprise software packages such as supply chain management information systems (SCM IS). Existing taxonomies have limited utility for software selection and analysis due to the variation and overlap in functionality found in modern enterprise systems.

A qualitative analysis of over 1,800 pages of SCM IS documentation and independent analyst reports is used to identify relevant SCM IS functional attributes in the seven most widespread SCM IS packages. Pattern matching and coding of constructs is used to iteratively build a hierarchical taxonomy of SCM IS functionality.

The taxonomy developed describes 83 major functional attributes that form five top‐level categories: primary supply chain processes, data management, decision support, relationship management, and performance improvement. The codes representing supply chain processes agree with the widely used Supply Chain Operations Reference (SCOR) process model, although the terminology was not used consistently in vendor and analyst documents.

The approach described enables richer classification schemes to be built that will better distinguish between the wide‐ranging functionality found in modern enterprise information systems.

Selection and analysis of SCM IS is difficult due to the functional overlaps in different systems. The approach described enables a more structured, detailed, and useful analysis of an organization's current or proposed information systems.

This paper contributes a novel approach for conceptualizing and analyzing complex information systems using hierarchical rather than traditional flat taxonomies.

The current review aims to examine the growth trajectory, most influential documents, intellectual and conceptual structure of the literature regarding gender issues in family business research.

The bibliometric analysis was performed using 224 documents from 1991 to 2020 extracted from the Web of Science database.

The review finds that this field's knowledge grew exponentially during the last three decades, mainly after 2003 and the last several years. Based on the co-citation analysis, three major research lines are identified: “Women's challenges and opportunities in the family business”, “Gender diversity in the family business corporate board”, and “Gender and family SMEs management.” The temporal co-word analysis reveals that “Gender diversity in the family business corporate board” is the latest research line.

By reviewing prominent cited references and documents that cited them, the authors provide the landscapes and research gaps of three major research lines for further development.

We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation. These include alternative random effects (RE) models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects (FE) conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypothesis that the sample initial conditions are exogenous is rejected by both samples. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The FE conditional logit estimates are similar to the estimates from the RE model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. For female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.

Explains that Canada is currently deciding whether to harmonize with US or international accounting standards and whether to allow Canadian SEC registrants to file their financial statements using US standards, outlines previous research on the information content of US/Canadian differences and tests the relative and incremental information content of 156 interlisted firms 1996‐1998. Explains the methodology and presents the results, which suggest that there is little difference in the relative information content of the two sets of standards although each provides information incremental to the other. Concludes that investors will not be harmed either by harmonization or by allowing financial reporting under US standards.

Social movements can disrupt existing industries and inspire the emergence of new markets by drawing attention to problems with the status quo and promoting alternatives. We examine how the influence of social movements on entrepreneurial activity evolves as the markets they foster mature. Theoretically, we argue that the success of social movements in furthering market expansion leads to three related outcomes. First, the movement-encouraged development of market infrastructure reduces the need for continued social movement support. Second, social movements’ efforts on behalf of new markets increase the importance of resource availability for market entry. Third, market growth motivates countermovement that reduce the beneficial impact of initiator movements on entrepreneurial activity. We test these arguments by analyzing evolving social movement dynamics and entrepreneurial activity in the US wind power industry from 1992 to 2007. We discuss the implications of our findings for the study of social movements, stakeholder management, sustainability, and entrepreneurship.

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This chapter provides sufficient conditions to identify the RD treatment effect using the mismeasured assignment variable, the treatment status and the outcome variable. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

Many family businesses do not survive into the second generation. A common reason put forward for this is poor succession planning for the second generation. This paper is designed with the aim to explore the role of the father in supporting the son’s business knowledge and development in Vietnamese family businesses.

This research adopted an inductive qualitative approach using multiple face-to-face semi-structured interviews with five father–son succession pairs. The interview participants were a cross section of Vietnamese family businesses, where the father–son pair was involved in the process of business knowledge transfer and the succession process was at an advanced stage.

The results suggest that the father plays different roles at different stages of the son’s business knowledge development process. In particular, the father acts as an example during the son’s childhood; a supporter to encourage the son to gain more business knowledge from both formal education and working experience outside the family business; a mentor and trouble-shooter after the son joins the family business as a full-time employee; and as an advisor after the son becomes the leader of the firm.

Most Vietnamese family businesses are still operating under the control of the first generation, and as a result, research into the succession process in Vietnam can help to provide valuable insights. Furthermore, existing research into the role of the predecessor in the whole process from the successor’s childhood until the end of the succession process is ambiguous and requires further research to clarify this research gap.

Bagging (bootstrap aggregating) is a smoothing method to improve predictive ability under the presence of parameter estimation uncertainty and model uncertainty. In Lee and Yang (2006), we examined how (equal-weighted and BMA-weighted) bagging works for one-step-ahead binary prediction with an asymmetric cost function for time series, where we considered simple cases with particular choices of a linlin tick loss function and an algorithm to estimate a linear quantile regression model. In the present chapter, we examine how bagging predictors work with different aggregating (averaging) schemes, for multi-step forecast horizons, with a general class of tick loss functions, with different estimation algorithms, for nonlinear quantile regression models, and for different data frequencies. Bagging quantile predictors are constructed via (weighted) averaging over predictors trained on bootstrapped training samples, and bagging binary predictors are conducted via (majority) voting on predictors trained on the bootstrapped training samples. We find that median bagging and trimmed-mean bagging can alleviate the problem of extreme predictors from bootstrap samples and have better performance than equally weighted bagging predictors; that bagging works better at longer forecast horizons; that bagging works well with highly nonlinear quantile regression models (e.g., artificial neural network), and with general tick loss functions. We also find that the performance of bagging may be affected by using different quantile estimation algorithms (in small samples, even if the estimation is consistent) and by using different frequencies of time series data.