Strategic divide management in pharmaceutical alliances: A partner’s strategic evolution as a reason for strategic divide

Strategic alliances are widely used in the pharmaceutical industry and, ideally, they are long-lasting structures that bring many benefits and value to the alliance partners. However, organizations continuously encounter pressures to enhance performance, while the environment in which they operate evolves. Therefore, an alliance partner might be forced to change its strategy, which can lead to the partners’ misaligned priorities and strategic divide. The academic literature acknowledges the impact a partner’s strategic change can have on the value of the alliance, but the phenomenon is not studied further, which is why the purpose of this study is to understand the role that a partner’s strategic evolution plays in strategic alliances within the pharmaceutical industry. The main purpose is further divided into three sub-objectives: 1) Describe reasons behind the strategic direction change of a partner firm, 2) Understand the consequences of partners’ misaligned priorities, and 3) Describe proactive and reactive ways to manage strategic divide between alliance partners. Since the phenomenon is not studied much, the empirical part of the study was conducted as a qualitative analysis using expert interviews to better understand, how the partner’s strategic evolution affects the alliance. The empirical data was organized into themes, according to the researcher’s interpretations on the interviews. The research findings demonstrated, how the partners change their strategies if the external or organizational environments change. The strategic changes, again, cause strategic divides between the alliance partners that are likely to have an impact on the alliance value. The findings revealed that the interviewees consider anticipation of the partner’s strategic change to be really difficult, but, at the same time, it was noted that a proactive strategic divide management could help to prevent and detect some divides. Additionally, the results showed that, after the detection, a reactive approach in a controlled manner was seen to be the most beneficial for the alliance’s future performance. This study proved that a partner’s strategic evolution affects the partners’ priority alignment and alliance value, which is why the strategic divide management is important in organizations that are involved with strategic alliances. In order to understand the role of a partner’s strategic evolution and provide managers with a tool to manage alliances and strategic divides, the study combined the alliance lifecycle as well as the proactive and reactive approaches to strategic divide, and presented a framework for strategic divide management.

IoT Based Pain Management using Physiological Parameters

The continuous technology evaluation is benefiting our lives to a great extent. The evolution of Internet of things and deployment of wireless sensor networks is making it possible to have more connectivity between people and devices used extensively in our daily lives. Almost every discipline of daily life including health sector, transportation, agriculture etc. is benefiting from these technologies. There is a great potential of research and refinement of health sector as the current system is very often dependent on manual evaluations conducted by the clinicians. There is no automatic system for patient health monitoring and assessment which results to incomplete and less reliable heath information. Internet of things has a great potential to benefit health care applications by automated and remote assessment, monitoring and identification of diseases. Acute pain is the main cause of people visiting to hospitals. An automatic pain detection system based on internet of things with wireless devices can make the assessment and redemption significantly more efficient. The contribution of this research work is proposing pain assessment method based on physiological parameters. The physiological parameters chosen for this study are heart rate, electrocardiography, breathing rate and galvanic skin response. As a first step, the relation between these physiological parameters and acute pain experienced by the test persons is evaluated. The electrocardiography data collected from the test persons is analyzed to extract interbeat intervals. This evaluation clearly demonstrates specific patterns and trends in these parameters as a consequence of pain. This parametric behavior is then used to assess and identify the pain intensity by implementing machine learning algorithms. Support vector machines are used for classifying these parameters influenced by different pain intensities and classification results are achieved. The classification results with good accuracy rates between two and three levels of pain intensities shows clear indication of pain and the feasibility of this pain assessment method. An improved approach on the basis of this research work can be implemented by using both physiological parameters and electromyography data of facial muscles for classification.