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A.J. Stapert
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How researchers evaluate opportunities and construct confidence under persistent uncertainty
Research-direction decision-making in AI-integrated biotech health technology innovation
Researchers in emerging biomedical innovation environments must frequently determine future research directions despite substantial uncertainty regarding feasibility, value, impact, and future applicability. Because these futures cannot yet be directly observed, researchers are often required to evaluate opportunities and commit resources before their eventual outcomes are known.
This study examines how researchers evaluate alternative research opportunities and navigate uncertainty when determining future research directions within an artificial intelligence (AI)-integrated biotech health technology innovation context. An exploratory qualitative research design was adopted using semi-structured interviews with eight researchers involved in the development of an early-stage precision medicine platform technology. The interview data were analysed using reflexive thematic analysis.
The findings suggest that uncertainty remains a persistent feature of research-direction evaluation rather than a condition that can be eliminated before decisions are made. Three interconnected processes were identified: collective judgement through distributed expertise, future-oriented opportunity evaluation, and evidence generation and interpretation. Through collective judgement, researchers draw upon expertise distributed across multiple actors and disciplines to assess uncertain opportunities. Through future-oriented opportunity evaluation, researchers assess future value, feasibility, impact, and relevance when comparing alternative directions. Through evidence generation and interpretation, researchers develop an empirical basis for assessing whether opportunities remain sufficiently credible and worthwhile to pursue.
A cross-theme analysis indicates that these processes collectively contribute to the construction of sufficient confidence despite uncertainty remaining unresolved. Confidence construction emerged as an interpretive concept that helps explain how researchers become able to move from evaluation to commitment before certainty has been achieved. The findings therefore suggest that research-direction decisions cannot be explained solely through uncertainty reduction and that confidence construction may help explain how commitment becomes possible under persistent uncertainty.
Although the study was conducted within an AI-integrated research environment, AI did not emerge as an independent basis for selecting research directions. Instead, AI functioned as a contextual condition that shaped expertise requirements, opportunity spaces, uncertainty conditions, and evidential capabilities. The study contributes to understanding how researchers evaluate opportunities, navigate uncertainty, and become able to commit to future research directions despite persistent uncertainty. Through the concept of confidence construction, it provides a process-oriented explanation of how commitment becomes possible when future outcomes remain uncertain. ...
This study examines how researchers evaluate alternative research opportunities and navigate uncertainty when determining future research directions within an artificial intelligence (AI)-integrated biotech health technology innovation context. An exploratory qualitative research design was adopted using semi-structured interviews with eight researchers involved in the development of an early-stage precision medicine platform technology. The interview data were analysed using reflexive thematic analysis.
The findings suggest that uncertainty remains a persistent feature of research-direction evaluation rather than a condition that can be eliminated before decisions are made. Three interconnected processes were identified: collective judgement through distributed expertise, future-oriented opportunity evaluation, and evidence generation and interpretation. Through collective judgement, researchers draw upon expertise distributed across multiple actors and disciplines to assess uncertain opportunities. Through future-oriented opportunity evaluation, researchers assess future value, feasibility, impact, and relevance when comparing alternative directions. Through evidence generation and interpretation, researchers develop an empirical basis for assessing whether opportunities remain sufficiently credible and worthwhile to pursue.
A cross-theme analysis indicates that these processes collectively contribute to the construction of sufficient confidence despite uncertainty remaining unresolved. Confidence construction emerged as an interpretive concept that helps explain how researchers become able to move from evaluation to commitment before certainty has been achieved. The findings therefore suggest that research-direction decisions cannot be explained solely through uncertainty reduction and that confidence construction may help explain how commitment becomes possible under persistent uncertainty.
Although the study was conducted within an AI-integrated research environment, AI did not emerge as an independent basis for selecting research directions. Instead, AI functioned as a contextual condition that shaped expertise requirements, opportunity spaces, uncertainty conditions, and evidential capabilities. The study contributes to understanding how researchers evaluate opportunities, navigate uncertainty, and become able to commit to future research directions despite persistent uncertainty. Through the concept of confidence construction, it provides a process-oriented explanation of how commitment becomes possible when future outcomes remain uncertain. ...
Researchers in emerging biomedical innovation environments must frequently determine future research directions despite substantial uncertainty regarding feasibility, value, impact, and future applicability. Because these futures cannot yet be directly observed, researchers are often required to evaluate opportunities and commit resources before their eventual outcomes are known.
This study examines how researchers evaluate alternative research opportunities and navigate uncertainty when determining future research directions within an artificial intelligence (AI)-integrated biotech health technology innovation context. An exploratory qualitative research design was adopted using semi-structured interviews with eight researchers involved in the development of an early-stage precision medicine platform technology. The interview data were analysed using reflexive thematic analysis.
The findings suggest that uncertainty remains a persistent feature of research-direction evaluation rather than a condition that can be eliminated before decisions are made. Three interconnected processes were identified: collective judgement through distributed expertise, future-oriented opportunity evaluation, and evidence generation and interpretation. Through collective judgement, researchers draw upon expertise distributed across multiple actors and disciplines to assess uncertain opportunities. Through future-oriented opportunity evaluation, researchers assess future value, feasibility, impact, and relevance when comparing alternative directions. Through evidence generation and interpretation, researchers develop an empirical basis for assessing whether opportunities remain sufficiently credible and worthwhile to pursue.
A cross-theme analysis indicates that these processes collectively contribute to the construction of sufficient confidence despite uncertainty remaining unresolved. Confidence construction emerged as an interpretive concept that helps explain how researchers become able to move from evaluation to commitment before certainty has been achieved. The findings therefore suggest that research-direction decisions cannot be explained solely through uncertainty reduction and that confidence construction may help explain how commitment becomes possible under persistent uncertainty.
Although the study was conducted within an AI-integrated research environment, AI did not emerge as an independent basis for selecting research directions. Instead, AI functioned as a contextual condition that shaped expertise requirements, opportunity spaces, uncertainty conditions, and evidential capabilities. The study contributes to understanding how researchers evaluate opportunities, navigate uncertainty, and become able to commit to future research directions despite persistent uncertainty. Through the concept of confidence construction, it provides a process-oriented explanation of how commitment becomes possible when future outcomes remain uncertain.
This study examines how researchers evaluate alternative research opportunities and navigate uncertainty when determining future research directions within an artificial intelligence (AI)-integrated biotech health technology innovation context. An exploratory qualitative research design was adopted using semi-structured interviews with eight researchers involved in the development of an early-stage precision medicine platform technology. The interview data were analysed using reflexive thematic analysis.
The findings suggest that uncertainty remains a persistent feature of research-direction evaluation rather than a condition that can be eliminated before decisions are made. Three interconnected processes were identified: collective judgement through distributed expertise, future-oriented opportunity evaluation, and evidence generation and interpretation. Through collective judgement, researchers draw upon expertise distributed across multiple actors and disciplines to assess uncertain opportunities. Through future-oriented opportunity evaluation, researchers assess future value, feasibility, impact, and relevance when comparing alternative directions. Through evidence generation and interpretation, researchers develop an empirical basis for assessing whether opportunities remain sufficiently credible and worthwhile to pursue.
A cross-theme analysis indicates that these processes collectively contribute to the construction of sufficient confidence despite uncertainty remaining unresolved. Confidence construction emerged as an interpretive concept that helps explain how researchers become able to move from evaluation to commitment before certainty has been achieved. The findings therefore suggest that research-direction decisions cannot be explained solely through uncertainty reduction and that confidence construction may help explain how commitment becomes possible under persistent uncertainty.
Although the study was conducted within an AI-integrated research environment, AI did not emerge as an independent basis for selecting research directions. Instead, AI functioned as a contextual condition that shaped expertise requirements, opportunity spaces, uncertainty conditions, and evidential capabilities. The study contributes to understanding how researchers evaluate opportunities, navigate uncertainty, and become able to commit to future research directions despite persistent uncertainty. Through the concept of confidence construction, it provides a process-oriented explanation of how commitment becomes possible when future outcomes remain uncertain.
Livestock production forms a threat to the environment and human health, while meat consumption increases due to global population and income growth. Commercial scale cultivated meat production provides a credible alternative due to its resemblance to the taste, texture and looks of conventional meat. In order to commercialize cultivated meat, major technological improvements are needed to scale-up its production from laboratory to commercial scale. This requires an animal cell line that can provide a food-grade product. Quail suspension cell line CCX.E10 is not genetically modified, is spontaneously immortalized and cultivated in animal component free medium. To research the growth kinetics of the cell line for commercial scale production investigation at a laboratory scale is required. Through this research a seed-train protocol to scale-up CCX.E10 cell bank to a scale-down model, namely a DASbox mini bioreactor system, was improved. Additionally, the growth kinetics of CCX.E10 were characterized during batch cultivation, while utilizing this system. A Design-of-Experiments approach was developed to understand what variables have the largest impact on growth kinetics of CCX.E10 to improve operational conditions. Input variables of the approach include the aeration rate, agitation rate and dissolved oxygen concentration of the bioreactor. This research found an exponential specific growth rate of 0.016 h-1 and viable cell density of 2.1x10^6 cells/mL for batch cultivation of cell line CCX.E10 in a DASbox bioreactor. A cell-specific lactate production rate 30% higher than the cell-specific glucose consumption rate was observed during the lag phase of cell cultivation. The growth kinetics derived during cell proliferation suggest that cell line CCX.E10 exhibits the Warburg effect. The findings indicate that the operational conditions for cultivated meat production based on CCX.E10 require further improvement to design a commercial scale bioprocess.
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Livestock production forms a threat to the environment and human health, while meat consumption increases due to global population and income growth. Commercial scale cultivated meat production provides a credible alternative due to its resemblance to the taste, texture and looks of conventional meat. In order to commercialize cultivated meat, major technological improvements are needed to scale-up its production from laboratory to commercial scale. This requires an animal cell line that can provide a food-grade product. Quail suspension cell line CCX.E10 is not genetically modified, is spontaneously immortalized and cultivated in animal component free medium. To research the growth kinetics of the cell line for commercial scale production investigation at a laboratory scale is required. Through this research a seed-train protocol to scale-up CCX.E10 cell bank to a scale-down model, namely a DASbox mini bioreactor system, was improved. Additionally, the growth kinetics of CCX.E10 were characterized during batch cultivation, while utilizing this system. A Design-of-Experiments approach was developed to understand what variables have the largest impact on growth kinetics of CCX.E10 to improve operational conditions. Input variables of the approach include the aeration rate, agitation rate and dissolved oxygen concentration of the bioreactor. This research found an exponential specific growth rate of 0.016 h-1 and viable cell density of 2.1x10^6 cells/mL for batch cultivation of cell line CCX.E10 in a DASbox bioreactor. A cell-specific lactate production rate 30% higher than the cell-specific glucose consumption rate was observed during the lag phase of cell cultivation. The growth kinetics derived during cell proliferation suggest that cell line CCX.E10 exhibits the Warburg effect. The findings indicate that the operational conditions for cultivated meat production based on CCX.E10 require further improvement to design a commercial scale bioprocess.