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This paper aims to address third actor introductions to interaction episodes aiming at fast-forwarding the continuous development of business relationships of new firms.

The study is qualitative, collecting data from 30 interviews from 28 informants associated with creation of new ventures and business network development in the context of a novel type of third actor called venture builder. Venture builders are privately owned organizations devoted to new firm creation in a factory-like mode, collaborating with individual entrepreneurs.

The findings suggest that interaction episodes, central to the development of new relationships, may be triggered by introductions managed by third actors using different types of involvement depending on the location and focus of the potential relationship. A framework is presented including four types of introductions to interaction episodes, aiming at saving time by removing the perceived distance between new firms and their counterparts in the initiation of business relationships. The framework describes four types of introductions of interaction episodes: Managed, Advised, Facilitated and Monitored.

Triggers and introductions of interaction episodes for new firms has previously been sparsely addressed. This paper presents how third actor involvement, by the introductions of interaction episodes with internal and external counterparts is managed with an aim of fast-forwarding relationship development.

Bundled payments for care are an efficient mechanism to align payer, provider, and patient incentives in the provision of health care services for an episode of care. In this chapter, we use agency theory to examine the evolution of bundled payment programs in private and public payer arrangements, and postulate future directions for bundled payment development as a key component in the provision and payment of health care services.

Deficits in fiscal and current account balances in a large number of countries reveal interesting implications of the causal relationship between internal and external imbalances. Empirical evidence about the occurrence of so-called twin deficits or twin surpluses provides crucial information about the validity of an intertemporal approach. However, most recent dynamic cyclical changes during the crisis period revealed many questions about the direct interconnection between macroeconomic performance and twin imbalances. In the paper we observe substantial features of twin imbalances in European transition economies. Event study (identification of large fiscal and current account changes and their parallel occurrence) and vector auto-regression methods will be employed to examine key aspects of twin imbalances. Our results suggest that current account deteriorations were predominately associated with negative public investment and savings balances (fiscal deficits), while current account improvements were predominately associated with positive private investment and savings balances, confirming empirical evidence about twin deficits in European transition economies.

Relationships are socially constructed by companies in interaction. This study explains the dynamic character of business-to-business relationships with the aid of rules theory, a theory borrowed from the communications field. Two forms of rules are identified: constitutive rules guide the interpretation of the other's acts, and regulative rules guide the appropriate response to the interpreted act. Rules theory asserts that companies act as if applying these rules. Relationships provide not only the context in which the parties’ acts are performed but are also the result of such acts. Thus, relationships are potentially reshaped each time one party performs an act and the other party gives meaning to that act and reacts.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.

The purpose of this paper is to consider the transition of surge episodes to stop episodes and differentiates between two types of surges, namely, surges that end in stops and surges that end in normal episodes.

Previous studies show that surges end in output contractions, crises, and reversals of capital inflows. However, when one looks closely at the data, more than half of surges end in normal episodes at least four quarters following the last surge quarter.

The results show the varying significance of global and domestic factors correlated with the occurrence of surges leading to stops and the size of gross inflows during these two types of surges.

The findings highlight the importance of differentiating between these two types of surges as it leaves scope for policy design in safeguarding financial stability amidst surging capital inflows.

The purpose of this paper is to discuss an online sensor-based support system which the authors believe can be useful in such scenarios. Persons with a cognitive impairment, such as those with Alzheimer’s disease, suffer from deficiencies in cognitive skills which reduce their independence; such patients can benefit from the provision of further assistance such as reminders for carrying out instrumental activities of daily living (IADLs).

The system proposed processes data from a network of sensors that have the capability of sensing user interactions and on-going IADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users’ behavioural patterns in performing activities. This probability model can underpin an intervention framework that prompts the user with the next step in the IADL when inactivity is being observed. This prompt for the next step is inferred from the conditional probability taken into consideration the IADL steps that have already been completed, in addition to contextual information relating to the time of day and the amount of time already spent on the activity. The originality of the work lies in combining partially observed sensor sequences and duration data associated with the IADLs. The prediction of the next step is then adjusted as further steps are completed and more time is spent towards the completion of the activity, thus updating the confidence that the prediction is correct. A reminder is only issued when there has been sufficient inactivity on the part of the patient and the confidence is high that the prediction is correct.

The results of this study verify that by including duration information the prediction accuracy of the model is increased and the confidence level for the next step in the IADL is also increased. As such, there is approximately a 10 per cent rise in the prediction performance in the case of single sensor activation in comparison to an alternative approach which did not consider activity durations.

Duration information to a certain extent has been widely ignored by activity recognition researchers and has received a very limited application within smart environments.

This study concludes that incorporating progressive duration information into partially observed sensor sequences of IADLs has the potential to increase performance of a reminder system for patients with a cognitive impairment, such as Alzheimer’s disease.

This paper aims to discuss an online sensor-based support system which is believed to be useful for persons with a cognitive impairment, such as those with Alzheimer’s disease, suffering from deficiencies in cognitive skills which reduce their independence. Such patients can benefit from the provision of further assistance such as reminders for carrying out instrumental activities of daily living (iADLs).

The system proposed processes data from a network of sensors that have the capability of sensing user interactions and ongoing iADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users’ behavioural patterns in performing activities. This probability model can underpin an intervention framework that prompts the user with the next step in the iADL when inactivity is being observed. This prompt for the next step is inferred from the conditional probability, taking into consideration the iADL steps that have already been completed, in addition to contextual information relating to the time of day and the amount of time already spent on the activity. The originality of the work lies in combining partially observed sensor sequences and duration data associated with the iADLs. The prediction of the next step is then adjusted as further steps are completed and more time is spent towards the completion of the activity; thus, updating the confidence that the prediction is correct. A reminder is only issued when there has been sufficient inactivity on the part of the patient and the confidence is high that the prediction is correct.

The results verify that by including duration information, the prediction accuracy of the model is increased, and the confidence level for the next step in the iADL is also increased. As such, there is approximately a 10 per cent rise in the prediction performance in the case of single-sensor activation in comparison to an alternative approach which did not consider activity durations. Thus, it is concluded that incorporating progressive duration information into partially observed sensor sequences of iADLs has the potential to increase performance of a reminder system for patients with a cognitive impairment, such as Alzheimer’s disease.

Activity duration information can be a potential feature in measuring the performance of a user and distinguishing different activities. The results verify that by including duration information, the prediction accuracy of the model is increased, and the confidence level for the next step in the activity is also increased. The use of duration information in online prediction of activities can also be associated to monitoring the deterioration in cognitive abilities and in making a decision about the level of assistance required. Such improvements have significance in building more accurate reminder systems that precisely predict activities and assist its users, thus, improving the overall support provided for living independently.