Understanding platform business models: A mixed methods study of marketplaces

Recent technological advances have enabled the emergence of novel business models based on digital platforms. Marketplace like Airbnb or Uber offer such digital platforms to connect previously unmatched demand-side and supply-side participants through innovative forms of value creation, delivery and capture. While countless firms claim to offer the next ‘Airbnb for X’ or ‘Uber for Y’, we lack knowledge about the defining business model characteristics of these marketplaces. To close the gap, this paper provides a conceptually and empirically grounded taxonomy of their business models. Applying a mixed methods approach, it first develops an integrative framework of marketplace business models. Guided by the framework, the research systematically analyzes 100 randomly selected marketplaces with content analysis and binary coding. The gathered data is analyzed with cluster analysis techniques to develop a taxonomy for marketplace business models. The clustering process reveals six clearly distinguishable types of marketplace business models and thus shows that there is no one-size-fits-all approach to creating, delivering, and capturing value with marketplaces and platforms in general. We characterize these distinctive types on basis of the qualitative and quantitative findings. Among others, we find that two of these types are highly aligned with business model characteristics associated with the so-called sharing economy. The findings are discussed against platform, marketplace, and sharing economy literature to contribute to a higher integration of different literature streams that are concerned with similar organizational types and phenomena.     

Approximate bounded influence estimation for longitudinal data with outliers and measurement errors

Mixed effects models or random effects models are popular for the analysis of longitudinal data. In practice, longitudinal data are often complex since there may be outliers in both the response and the covariates and there may be measurement errors. The likelihood method is a common approach for these problems but it can be computationally very intensive and sometimes may even be computationally infeasible. In this article, we consider approximate robust methods for nonlinear mixed effects models to simultaneously address outliers and measurement errors. The approximate methods are computationally very efficient. We show the consistency and asymptotic normality of the approximate estimates. The methods can also be extended to missing data problems. An example is used to illustrate the methods and a simulation is conducted to evaluate the methods.     

Prediction of Times to Failure of Censored Items for a Simple Step-Stress Model with Regular and Progressive Type I Censoring from the Exponential Distribution

The problem of predicting times to failure of units from the Exponential Distribution which are censored under a simple step-stress model is considered in this article. We discuss two types of censoring—regular and progressive Type I—and two kinds of predictors—the maximum likelihood predictors (MLP) and the conditional median predictors (CMP) for each type of censoring. Numerical examples are used to illustrate the prediction methods. Using simulation studies, mean squared prediction error (MSPE) and prediction intervals are generated for these examples. MLP and the CMP are then compared with respect to MSPE and the prediction interval.     

Point predictors of the latent failure times of censored units in progressively Type-II censored competing risks data from the exponential distributions

In this paper, we discuss the problem of predicting times to the latent failures of units censored in multiple stages in a progressively Type-II censored competing risks model. It is assumed that the lifetime distribution of the latent failure times are independent and exponential-distributed with the different scale parameters. Several classical point predictors such as the maximum likelihood predictor, the best unbiased predictor, the best linear unbiased predictor, the median unbiased predictor and the conditional median predictor are obtained. The Bayesian point predictors are derived under squared error loss criterion. Moreover, the point estimators of the unknown parameters are obtained using the observed data and different point predictors of the latent failure times. Finally, Monte-Carlo simulations are carried out to compare the performances of the different methods of prediction and estimation and one real data is used to illustrate the proposed procedures.     

Small area estimation of mean price of habitation transaction using time-series and cross-sectional area-level models

In this paper, a new small domain estimator for area-level data is proposed. The proposed estimator is driven by a real problem of estimating the mean price of habitation transaction at a regional level in a European country, using data collected from a longitudinal survey conducted by a national statistical office. At the desired level of inference, it is not possible to provide accurate direct estimates because the sample sizes in these domains are very small. An area-level model with a heterogeneous covariance structure of random effects assists the proposed combined estimator. This model is an extension of a model due to Fay and Herriot [5], but it integrates information across domains and over several periods of time. In addition, a modified method of estimation of variance components for time-series and cross-sectional area-level models is proposed by including the design weights. A Monte Carlo simulation, based on real data, is conducted to investigate the performance of the proposed estimators in comparison with other estimators frequently used in small area estimation problems. In particular, we compare the performance of these estimators with the estimator based on the Rao–Yu model [23]. The simulation study also accesses the performance of the modified variance component estimators in comparison with the traditional ANOVA method. Simulation results show that the estimators proposed perform better than the other estimators in terms of both precision and bias.     

A bivariate distribution with Lomax and geometric margins

We develop a stochastic model describing the joint distribution of $(X,N)$, where $N$ has a geometric distribution while $X$ is the sum of $N$ dependent, heavy-tail Pareto components. Models of this form arise in many applications, ranging from hydro-climatology to finance and insurance. We present fundamental properties of this vector, including marginal and conditional distributions, moments, representations, and parameter estimation. We also include an example from finance, illustrating modeling potential of this new bivariate distribution.     

The Role of Non‐profit Organizations in the Mixed Economy of Welfare‐to‐Work in the UK and Australia

This article charts the development of welfare‐to‐work policies and compares and contrasts the traditions of delivery in the UK and Australia. We find that in the UK, employment services and social security benefit administration have been dominated by the central state, traditionally affording a key role to civil servants as direct delivery agents. However, in federal Australia, mixed economies of welfare‐to‐work operate in the different states, there is a far greater role for social services and non‐profit organizations are firmly established as key providers of frontline employment services. Since the late 1990s, UK welfare reforms have been gradually following the Australian lead in contracting non‐state actors as delivery agents. As this trend seems set to continue and intensify, we examine the Australian experience in order to reflect on the role of non‐profits in policy reform.     

包含加法模型和混合模型的指数分析体系

统计指数体系分析方法的一个较大不足就是局限于乘法形式的经济方程式,然而经济现象之间的相互联系是多种多样的,目前的统计指数体系分析方法难以应用于形式多样的复杂经济现象的因素分析。本文把基于乘法方式的指数体系称之为指数体系的“乘法模型”,并以此为基础,将传统的只包含乘法关系的经济方程式加以扩展,将其推广到可以应用于加减运算的“加法模型”和包含全部四则运算的“混合模型”,建立了“广义指数分析模型”。该模型不仅保留了传统统计指数体系的基本结构和基本分析方法,还可以较大地提高统计指数分析的深度和广度,由此可以基本解决统计指数体系分析方法的局限性问题。     

On the choice of instruments in mixed frequency specification tests

Abstract

Time averaging has been the traditional approach to handle mixed sampling frequencies. However, it ignores information possibly embedded in high frequency. Mixed data sampling (MIDAS) regression models provide a concise way to utilize the additional information in high-frequency variables. In this paper, we propose a specification test to choose between time averaging and MIDAS models, based on a Durbin-Wu-Hausman test. In particular, a set of instrumental variables is proposed and theoretically validated when the frequency ratio is large. As a result, our method tends to be more powerful than existing methods, as reconfirmed through the simulations.