Adaptation Learning: An Ambidextrous Perspective

The dissertation examines whether adaptation, through ambidexterity, helps firms improve their performance. Adaptation ambidexterity is an intrafirm process of balancing and integrating exploration and exploitation learning in a firm's partner-specific investment strategy to develop products according to that partner's changing requirements. Specific research questions are whether: (1) adaptation ambidexterity improves new product performance, and (2) whether the marketing environment characteristics of adaptation (market turbulence, technological turbulence, and partner dependence) affect that relationship. To address these concerns, the dissertation develops scales for adaptation ambidexterity, adaptation balance and adaptation integration. Then, moderated regression is used for main effects and moderation effects. The study employs a cross-sectional design and examines the hypothetical relationships. Key participants to be surveyed were determined using a random list of US high-tech manufacturing firms. The results show that adaptation ambidexterity is an important factor that influences new product performance. First, adaptation integration, one of the two components of being ambidextrous, has strong and consistent effects on new product performance. Second, under low market turbulence, low technological turbulence, and low partner dependence adaptation balance may in fact negatively affect performance. Under the high levels of these factors, adaptation balance is non-detrimental condition for ambidexterity. This finding confirms the fact that being balanced without justification may harm business performance. Finally, the negative interaction effect of adaptation integration and technological turbulent environment in this study in fact suggest that adaptation integration is more effective in low technological turbulence that in high technological turbulence.Department of Marketin

Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability

A comparative impact evaluation of two human resource models for community-based active tuberculosis case finding in Ho Chi Minh City, Viet Nam

Background: To achieve the WHO End TB Strategy targets, it is necessary to detect and treat more people with active TB early. Scale–up of active case finding (ACF) may be one strategy to achieve that goal. Given human resource constraints in the health systems of most high TB burden countries, volunteer community health workers (CHW) have been widely used to economically scale up TB ACF. However, more evidence is needed on the most cost-effective compensation models for these CHWs and their potential impact on case finding to inform optimal scale-up policies. Methods: We conducted a two-year, controlled intervention study in 12 districts of Ho Chi Minh City, Viet Nam. We engaged CHWs as salaried employees (3 districts) or incentivized volunteers (3 districts) to conduct ACF among contacts of people with TB and urban priority groups. Eligible persons were asked to attend health services for radiographic screening and rapid molecular diagnosis or smear microscopy. Individuals diagnosed with TB were linked to appropriate care. Six districts providing routine NTP care served as control area. We evaluated additional cases notified and conducted comparative interrupted time series (ITS) analyses to assess the impact of ACF by human resource model on TB case notifications. Results: We verbally screened 321,020 persons in the community, of whom 70,439 were eligible for testing and 1138 of them started TB treatment. ACF activities resulted in a + 15.9% [95% CI: + 15.0%, + 16.7%] rise in All Forms TB notifications in the intervention areas compared to control areas. The ITS analyses detected significant positive post-intervention trend differences in All Forms TB notification rates between the intervention and control areas (p = 0.001), as well as between the employee and volunteer human resource models (p = 0.021). Conclusions: Both salaried and volunteer CHW human resource models demonstrated additionality in case notifications compared to routine case finding by the government TB program. The salaried employee CHW model achieved a greater impact on notifications and should be prioritized for scale-up, given sufficient resources

Introducing risk inequality metrics in tuberculosis policy development.

Global stakeholders including the World Health Organization rely on predictive models for developing strategies and setting targets for tuberculosis care and control programs. Failure to account for variation in individual risk leads to substantial biases that impair data interpretation and policy decisions. Anticipated impediments to estimating heterogeneity for each parameter are discouraging despite considerable technical progress in recent years. Here we identify acquisition of infection as the single process where heterogeneity most fundamentally impacts model outputs, due to selection imposed by dynamic forces of infection. We introduce concrete metrics of risk inequality, demonstrate their utility in mathematical models, and pack the information into a risk inequality coefficient (RIC) which can be calculated and reported by national tuberculosis programs for use in policy development and modeling

Establishing and validating noninvasive prenatal testing procedure for fetal aneuploidies in Vietnam

Noninvasive prenatal testing (NIPT) for fetal aneuploidies has been widely adopted in developed countries. Despite the sharp decrease in the cost of massively parallel sequencing, the technical know-how and skilled personnel are still one of the major limiting factors for applying this technology to NIPT in low-income settings. Here, we present the establishment and validation of our NIPT procedure called triSure for detection of fetal aneuploidies.We established the triSure algorithm based on the difference in proportion of fetal and maternal fragments from the target chromosome to all chromosomes. Our algorithm was validated using a published data set and an in-house data set obtained from high-risk pregnant women in Vietnam who have undergone amniotic testing. Several other aneuploidy calling methods were also applied to the same data set to benchmark triSure performance.The triSure algorithm showed similar accuracy to size-based method when comparing them using published data set. Using our in-house data set from 130 consecutive samples, we showed that triSure correctly identified the most samples (overall sensitivity and specificity of 0.983 and 0.986, respectively) compared to other methods tested including count-based, sized-based, RAPIDR and NIPTeR.We have demonstrated that our triSure NIPT procedure can be applied to pregnant women in low-income settings such as Vietnam, providing low-risk screening option to reduce the need for invasive diagnostic tests

Enhanced Private Sector Engagement for Tuberculosis Diagnosis and Reporting through an Intermediary Agency in Ho Chi Minh City, Viet Nam

Under-detection and -reporting in the private sector constitute a major barrier in Viet Nam’s fight to end tuberculosis (TB). Effective private-sector engagement requires innovative approaches. We established an intermediary agency that incentivized private providers in two districts of Ho Chi Minh City to refer persons with presumptive TB and share data of unreported TB treatment from July 2017 to March 2019. We subsidized chest x-ray screening and Xpert MTB/RIF testing, and supported test logistics, recording, and reporting. Among 393 participating private providers, 32.1% (126/393) referred at least one symptomatic person, and 3.6% (14/393) reported TB patients treated in their practice. In total, the study identified 1203 people with TB through private provider engagement. Of these, 7.6% (91/1203) were referred for treatment in government facilities. The referrals led to a post-intervention increase of +8.5% in All Forms TB notifications in the intervention districts. The remaining 92.4% (1112/1203) of identified people with TB elected private-sector treatment and were not notified to the NTP. Had this private TB treatment been included in official notifications, the increase in All Forms TB notifications would have been +68.3%. Our evaluation showed that an intermediary agency model can potentially engage private providers in Viet Nam to notify many people with TB who are not being captured by the current system. This could have a substantial impact on transparency into disease burden and contribute significantly to the progress towards ending TB