Understanding the role of organizational integration in developing and operating Software-as-a-Service

While previous research has mostly studied Software-as-a-Service from a client perspective or focused on downstream activities of vendors, an in-depth understanding of the coordination and communication between software development and software operations is still missing. In order to fill this gap, we develop a theoretical framework that integrates extant literature on the constituent characteristics of services and on organizational integration. Guided by this framework and based on a multiple-case study of six software vendors, we identify five types of challenges and opportunities of organizational integration between Software-as-a-Service development and operations. The challenges include ensuring awareness and continuity between operations and development, as well as considering customers’ business activities in scheduling updates. The opportunities relate to the ability to leverage a direct feedback channel and innovate with higher velocity. In addition, service mindset, technical harmonization, and company size emerged as three contingency factors that enable or inhibit organizational integration. The study contributes to existing literature by providing a better understanding of the activities that require coordination and communication when developing and operating Software-as-a-Service. Moreover, this study adds to previous research by linking the integration of organizational subunits to constituent characteristics of services. Decision makers learn about challenges and opportunities when offering Software-as-a-Service that go beyond providing a technical infrastructure.     

Strategies for Fitting Large,Geostatistical Data in MCMC Simulation

Models for geostatistical data introduce spatial dependence in the covariance matrix of location-specific random effects. This is usually defined to be a parametric function of the distances between locations. Bayesian formulations of such models overcome asymptotic inference and estimation problems involved in maximum likelihood-based approaches and can be fitted using Markov chain Monte Carlo (MCMC) simulation. The MCMC implementation, however, requires repeated inversions of the covariance matrix which makes the problem computationally intensive, especially for large number of locations. In the present work, we propose to convert the spatial covariance matrix to a sparse matrix and compare a number of numerical algorithms especially suited within the MCMC framework in order to accelerate large matrix inversion. The algorithms are assessed empirically on simulated datasets of different size and sparsity. We conclude that the band solver applied after ordering the distance matrix reduces the computational time in inverting covariance matrices substantially.     

Type-I-error rate inflation in mixed models for repeated measures caused by ambiguous or incomplete model specifications

Pre-specification of the primary analysis model is a pre-requisite to control the family-wise type-I-error rate (T1E) at the intended level in confirmatory clinical trials. However, mixed models for repeated measures (MMRM) have been shown to be poorly specified in study protocols. The magnitude of a resulting T1E rate inflation is still unknown. This investigation aims to quantify the magnitude of the T1E rate inflation depending on the type and number of unspecified model items as well as different trial characteristics. We simulated a randomized, double-blind, parallel group, phase III clinical trial under the assumption that there is no treatment effect at any time point. The simulated data was analysed using different clusters, each including several MMRMs that are compatible with the imprecise pre-specification of the MMRM. T1E rates for each cluster were estimated. A significant T1E rate inflation could be shown for ambiguous model specifications with a maximum T1E rate of 7.6% [7.1%; 8.1%]. The results show that the magnitude of the T1E rate inflation depends on the type and number of unspecified model items as well as the sample size and allocation ratio. The imprecise specification of nuisance parameters may not lead to a significant T1E rate inflation. However, the results of this simulation study rather underestimate the true T1E rate inflation. In conclusion, imprecise MMRM specifications may lead to a substantial inflation of the T1E rate and can damage the ability to generate confirmatory evidence in pivotal clinical trials.     

Equity Crowdfunding: The Influence of Perceived Innovativeness on Campaign Success

This paper examines the impact of perceived innovativeness on the success of equity crowdfunding campaigns. Building on the investor perspective, we hypothesize a positive impact of perceived innovativeness on the campaign outcome. Our database covers 191 campaigns launched in France on different platforms, drawing on over 2,000 individual assessments of the perceived innovativeness of the start-ups involved, carried out by 176 participants with diverse backgrounds. We find support for our hypothesis from the investor perspective in that highly innovative projects attract more crowd investors and, in turn, raise more capital. We contribute to the understanding of how the crowd makes investment choices.