An approach to user knowledge acquisition in product design

Tan, Libin; Zhang, Haijuan

doi:10.1016/j.aei.2021.101408

Cited by 16 publications

(3 citation statements)

References 80 publications

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“…Therefore, organizational iterative learning is not only an important learning mode product development, but also an important means to cope with market competition and environmental mutations. Organizational iterative learning enables entrepreneurs to focus on the assessment and prediction of customer needs, relying on market forces to solve problems that enterprises cannot solve [ 47 ]. The lean startup strategy enables enterprises to further clarify market trends, correct errors in the R&D process, provide guarantee and learning foundation for organizational iterative learning of "response-interaction-trial and error-optimization", continuously stimulate new ideas, reduce entrepreneurial uncertainty, and promote sustainable development of entrepreneurship.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Can adopting lean startup strategy promote the sustainable development of new ventures? The mediating role of organizational iterative learning

Zhuge,

He,

Yuan

et al. 2023

PLoS ONE

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When high uncertainty becomes the norm in entrepreneurship, entrepreneurial failure often becomes the first natural barrier that new ventures face. In dynamic environment, there is still a lack of clear answers on what strategic orientation new ventures need to adopt to improve organizational learning efficiency and achieve sustainable development. Lean startup theory believes that the entrepreneurial process is also a process where organizational cognition is constantly iterated and updated, which drives entrepreneurs to explore business opportunities through iteration learning and early customer insight. The paper aims to describe and examine the relationship between lean startup strategy, organizational iterative learning, and sustainable development of new ventures. This model is tested on the survey data of R&D departments of 325 technology new ventures in China. The research results show that: lean startup strategy can positively affect sustainable performance of new ventures; organizational iterative learning plays a mediating role in the relationship between lean startup strategy and sustainable development; market dynamics positively moderate the relationship between organizational iterative learning and sustainable development, while technology dynamics negatively moderate this relationship; furthermore, the two also moderate the process of lean startup strategy influencing sustainable development through organizational iterative learning, and that moderated mediating effect is significant. The research results reveal that entrepreneurs should deepen lean startup practices in new business layout, advocate the iteration and output of advanced knowledge, help enterprises establish proprietary knowledge barriers, and achieve sustainable development.

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Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Can adopting lean startup strategy promote the sustainable development of new ventures? The mediating role of organizational iterative learning

Zhuge,

He,

Yuan

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…A 2017 study [14] found approximately 7000 papers published on sentiment analysis by 2016, including work on sarcasm detection, emotion mining, and sentiment analysis applications. In industry, sentiment analysis is implemented for pattern identification and accurate data-driven predictions [15], providing solutions to different real-life problems, such as stock market prediction [16], political elections forecasting [17], crisis management [18], individual and collective emotional response communication [19], etc. In the study of Asur and Huberman [10], they argued that a deep understanding of social media communications could be helpful for accurate predictions of future activities and events.…”

Section: Sentiment Analysismentioning

confidence: 99%

Public Health Policy Monitoring through Public Perceptions: A Case of COVID-19 Tweet Analysis

Renda

Yusuf

et al. 2022

Information

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Since the start of the COVID-19 pandemic, government authorities have responded by issuing new public health policies, many of which were intended to contain its spread but ended up limiting economic and social activities. The citizen responses to these policies are diverse, ranging from goodwill to fear and anger. It is challenging to determine whether or not these public health policies achieved the intended impact. This requires systematic data collection and scientific studies, which can be very time-consuming. To overcome such challenges, in this paper, we provide an alternative approach to continuously monitor and dynamically make sense of how public health policies impact citizens. Our approach is to continuously collect Twitter posts related to COVID-19 policies and to analyze the public reactions. We have developed a web-based system that collects tweets daily and generates timelines and geographical displays of citizens’ “concern levels”. Tracking the public reactions towards different policies can help government officials assess the policy impacts in a more dynamic and real-time manner. For this paper, we collected and analyzed over 16 million tweets related to ten policies over a 10-month period. We obtained several findings; for example, the “COVID-19 (General)” and ”Ventilators” policies engendered the highest concern levels, while the “Face Coverings” policy caused the lowest. Nine out of ten policies exhibited significant changes in concern levels during the observation period.

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“…Regarding the problem of analyzing the demand indexes for age-friendly of products, the research mainly focuses on two aspects. (1) Requirements acquisition: Translating users' design expectations for smart products into canonical expressions of specific requirements [6]. (2) Requirements importance assessment: An assessment method is used to identify the satisfaction of requirements, and the prioritization of requirements is formed based on the results [7].…”

Section: Introductionmentioning

confidence: 99%

Older Users Acceptance of Smart Products: An Extension of the Technology Acceptance Model

Zeng,

Zhu,

Lin

et al. 2024

IEEE Access

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In response to the challenge of accurately assessing age-friendly design requirements during the improvement phase of smart products, a novel evaluation model is proposed. This model is grounded in the technology acceptance model and considers the interplay between user weights and requirements. The aim is to establish precise product indicators and development strategies, thereby maximizing user satisfaction and market competitiveness. Initially, design evaluation indicators are derived from the technology acceptance model. Subsequently, the TextRank algorithm and grounded theory are employed to determine the initial weights of these indicators. The DEMATEL method is utilized to analyze the weight of influence relationships between indicators, while the Kano model determines importance coefficients for adjusting weights. Through normalization, final weights are assigned, and the TOPSIS method implements sorting and significance analysis of age-friendly design requirements. Five age-friendly smartphone samples serve as case studies to evaluate and analyze the five age-friendly design requirement indicators. Method comparison and advantage/disadvantage sorting analyses are conducted to validate the effectiveness and feasibility of the proposed approach.

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An approach to user knowledge acquisition in product design

Cited by 16 publications

References 80 publications

Can adopting lean startup strategy promote the sustainable development of new ventures? The mediating role of organizational iterative learning

Can adopting lean startup strategy promote the sustainable development of new ventures? The mediating role of organizational iterative learning

Public Health Policy Monitoring through Public Perceptions: A Case of COVID-19 Tweet Analysis

Older Users Acceptance of Smart Products: An Extension of the Technology Acceptance Model

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