This article analyzes the use and limitations of nonmonetary contract incentives in managing third-party accountability in human services. In-depth case studies of residential care homes for the elderly and integrated family service centers, two contrasting contracting contexts, were conducted in Hong Kong. These two programs vary in service programmability and service interdependency. In-depth interviews with 17 managers of 48 Residential Care Homes for the Elderly (RCHEs) and 20 managers of 10 Integrated Family Service Centers (IFSCs) were conducted. Interviews with the managers show that when service programmability was high and service interdependency was low, nonmonetary contract incentives such as opportunities for self-actualization professionally or reputation were effective in improving service quality from nonprofit and for-profit contractors. When service programmability was low and service interdependency was high, despite that only nonprofit organizations were contracted, many frontline service managers reported that professional accountability was undermined by ambiguous service scope, performance emphasis on case turnover, risk shift from public service units and a lack of formal accountability relationships between service units in the service network. The findings shed light on the limitations of nonmonetary contract incentives.

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.