Usage Continuance in Software-as-a-Service

2022 | Information Systems Frontiers | Citations: 0

Authors: Baumann, Elias; Kern, Jana; Lessmann, Stefan

Abstract: Software-as-a-service applications are experiencing immense growth as their comp ... Expand

Abstract: Software-as-a-service applications are experiencing immense growth as their comparatively low cost makes them an important alternative to traditional software. Following the initial adoption phase, vendors are now concerned with the continued usage of their software. To analyze the influence of different measures to improve continued usage over time, a longitudinal study approach using data from a SaaS vendor was implemented. Employing a linear mixed model, the study finds several measures to have a positive effect on a software’s usage penetration. In addition to these activation measures performed by the SaaS vendor, software as well as client characteristics were also examined, but did not display significant estimates. The findings emphasize the need for proactive activation initiatives to raise usage penetration. More generally, the study contributes novel insights into the scarcely researched field of influencing factors on SaaS usage continuance. Collapse

Topics: software as a service knowledge management usage intention single sign on intranet

Methods: longitudinal research hierarchical linear modeling qualitative interview survey literature study

Just DM Me (Politely): Direct Messaging, Politeness, and Hiring Outcomes in Online Labor Markets

2021 | Information Systems Research | Citations: 1

Authors: Hong, Yili; Peng, Jing; Burtch, Gordon; Huang, Ni

Abstract: This study examines the role of text-based direct messaging systems in online la ... Expand

Abstract: This study examines the role of text-based direct messaging systems in online labor markets, which provide a communication channel between workers and employers, adding a personal touch to the exchange of online labor. We propose the effect of workers’ use of the direct messaging system on employers’ hiring decisions and conceptualize the information role of direct messaging. To empirically evaluate the information role of the direct messaging system, we leverage data on the direct messaging activities between workers and employers across more than 470,000 job applications on a leading online labor market. We report evidence that direct messaging with a prospective employer increases a worker’s probability of being hired by 8.9%. However, the degree to which workers benefit from direct messaging is heterogeneous, and the effect amplifies for workers approaching employers from a position of disadvantage (lacking tenure or fit with the job) and attenuates as more workers attempt to message the same prospective employer. The effects also depend on message content. In particular, we find that the benefits of direct messaging for workers depend a great deal on the politeness of the workers, and this “politeness effect” depends on several contextual factors. The beneficial effects are amplified for lower-status workers (i.e., workers lacking tenure and job fit) and workers who share a common language with the employer. At the same time, the beneficial effects weaken in the presence of typographical errors. These findings provide important insights into when and what to message to achieve favorable hiring outcomes in online employment settings. Collapse

Topics: online labor market information overload platform worker database system PHP

Methods: data imputation instrumental variables estimation machine learning simulation XGBoost

Theories: lemon market theory

Deep learning for decision making and the optimization of socially responsible investments and portfolio

2019 | Decision Support Systems | Citations: 1

Authors: Vo, Nhi N. Y.; He, Xuezhong; Liu, Shaowu; Xu, Guandong

Abstract: A socially responsible investment portfolio takes into consideration the environ ... Expand

Abstract: A socially responsible investment portfolio takes into consideration the environmental, social and governance aspects of companies. It has become an emerging topic for both financial investors and researchers recently. Traditional investment and portfolio theories, which are used for the optimization of financial investment portfolios, are inadequate for decision-making and the construction of an optimized socially responsible investment portfolio. In response to this problem, we introduced a Deep Responsible Investment Portfolio (DRIP) model that contains a Multivariate Bidirectional Long Short-Term Memory neural network, to predict stock returns for the construction of a socially responsible investment portfolio. The deep reinforcement learning technique was adapted to retrain neural networks and rebalance the portfolio periodically. Our empirical data revealed that the DRIP framework could achieve competitive financial performance and better social impact compared to traditional portfolio models, sustainable indexes and funds. Collapse

Topics: financial performance decision support system organizational productivity artificial intelligence analytical information system

Methods: artificial neural network reinforcement learning deep learning machine learning longitudinal research

Theories: portfolio theory

Multiperiod forecasting in stock markets: a paradox solved

2004 | Decision Support Systems | Citations: 2

Authors: Krolzig, Hans-Martin; Toro, Juan

Abstract: One of the most striking results on asset pricing in the last 20 years is the be ... Expand

Abstract: One of the most striking results on asset pricing in the last 20 years is the better forecastability of long-horizon returns over one-step return forecasts. This could seem a paradox, given that the further our forecast horizon the greater the uncertainty we are bound to face. This point can be found in Campbell and Shiller [Journal of Finance 43 (1988) 661; Journal of Finance 40 (1985) 793; American Economic Review 76 (1986) 1142] among others. In this paper, we offer an alternative explanation to this “forecast paradox ” that is in agreement with Kim et al. [Review of Economic Studies 30 (1992) 25], who found that the negative serial correlation in long-horizon returns depends very much on the sample choice. Our explanation is based on the existence of simultaneous shifts in the time series of the equilibrium stock price and dividends. This explanation relies on the concept of co-breaking [D.F. Hendry, A theory of co-breaking, Mimeo, Nuffield College, Oxford, 1995.]. We put forward a stochastic present value model, in which we are able to show how shifts in the process for dividends lead to shifts in the equilibrium stock price. This has important implications for multiperiod forecasting, as we demonstrate in this paper. An empirical application supports our results. In our empirical application, we model earning, dividends, stock prices, and the risk-free interest in the United States from 1926 to 1985. We can distinguish three different historical periods where the process for dividends and the equilibrium stock prices are characterized by different properties in terms of their means and variances. Our empirical model is extended to a forecasting exercise where the “forecast paradox” is solved. Collapse

Topics: price management armed conflict innovation management system support

Methods: longitudinal research vector autoregression stochastic model likelihood-ratio test experiment