Management approaches in the field of smart
Mohammadakbar Sheikhzadeh; Mohammad Taghi Taghavifard; iman raeisivanani; Jahanyar Bamdadsoofi
Abstract
Crowdfunding is an effective way to achieve the non-profit goals of the charities which can be expanded using information and communication technology. In this regard, this study was conducted with the aim of providing an online donation intention model for collective financing of charitable institutions ...
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Crowdfunding is an effective way to achieve the non-profit goals of the charities which can be expanded using information and communication technology. In this regard, this study was conducted with the aim of providing an online donation intention model for collective financing of charitable institutions in Iran. The current research is an applied-developmental research in terms of its purpose, and it is considered a cross-sectional survey research from the data collection point of view. The statistical population in the qualitative section includes managers of charity institutions and university professors. Sampling was done using a purposeful method and theoretical saturation with 10 interviews. In the quantitative section, the views of 357 benefactors were used. The data collection tool is a semi-structured interview and a researcher-made questionnaire. To analyze the collected data, qualitative thematic analysis and partial least squares were used. The research findings showed that the technical infrastructure facilitating conditions, the quality and transparency of the website information, the security of the site and the application, and the preservation of privacy affect the perceived risk. Perceived risk affects the expectation of effort and the pleasure of helping others and leads to the expectation of performance and social influence. Finally, online donation intention is strengthened through trust.IntroductionToday, charity institutions in the country can expose to the various needs of the needy to the public using crowdfunding platforms, so that with the participation of people the necessary financial resources are provided and the problems of the needy and the deprived are solved. In this field, practical and scientific activities have also been carried out however one of the most important issues neglected from the researchers’ view point and activists in this field is the discussion of the desire or intention of online donation among benefactors. The best online donation platforms and the use of the latest technologies are not enough to attract public participation, and it is not easy to encourage and motivate people to donate online. This issue itself can be discussed from various aspects. On the one hand, the basic element of this method of financing is the maximum participation of people, and on the other hand, members of the society have little knowledge about the issue and do not have much desire to attend such calls.Therefore, in general, it can be said that the intention to donate online is a fundamental factor in the success of the efforts of the country's charities for crowdfunding. The basic question of this research is that: what is the online donation intention model for collective financing of charities in Iran?Literature ReviewCrowdfunding is an alternative way to finance a project with specific goals at a specific time through an online platform. The phenomenon of crowdfunding is emerging in the field of financing resources, goods and services in the modern digital field (Jiao and Yue, 2021). The current study focuses on the crowdfunding approach based on donations in Iranian charities. This method is mostly used in non-profit and philanthropic projects, and the participants do not expect any financial support, and are mostly used to finance charities, religious places, and civil institutions (Salido et al., 2021). In the crowdfunding method based on donation, the participants do not have any expectations for the provided financial support. The donor believes wholeheartedly in the correctness of his act and considers it to be socially beneficial. This model is usually used in financing civil institutions and charities (Van Thienbroek et al., 2023).MethodologyThis is an applied-developmental research. The population of participants in the qualitative section includes theoretical experts and experimental. Sampling was done with a purposeful method and theoretical saturation was achieved with 10 interviews. The sample size was estimated to be 357 people using Cochran's formula.The main tool for collecting research data is a semi-structured interview and a researcher-made questionnaire. And then, using ISM, the relationships between the main factors affecting the intention to donate online were determined by experts. The interview included 6 basic questions and was conducted in a semi-structured way. The research questionnaire includes 10 main constructs and 51 items with a five-point Likert scale. Thematic analysis was used to identify the categories of online donation intention for crowdfunding of charities in Iran. Data analysis was done in qualitative phase with MAXQDA software and in quantitative phase with Smart PLS software.ResultsThe results of the interviews were analyzed by the qualitative method of thematic analysis based on the six-step method of Etrid-Straling (2001). 830 open codes were identified in the first stage. After analysis and review, we reached 5 overarching themes, 10 organizing themes and 51 basic themes as below:Overarching themes: Social factors- Technical factors-Individual factors- Security factors- Consequence factors.Organizer themes: Performance expectation- Endeavour expectation- Social influence- Quality and transparency of website information- Technical infrastructure facilitating conditions- Trust- The joy of helping others- Perceived risk- Site and application security and privacy- Intention to donate online.DiscussionThe present study was conducted with the aim of presenting the online donation intention model for the collective financing of charitable organizations in Iran. Based on the results, it was determined that the facilitating conditions of the technical infrastructure, the quality and transparency of the website information, the security of the site and the application, and the protection of privacy affect the perceived risk. In the results of Zhang et al.'s (2023) and Shahidi and Kivani's (1401) studies, the technical infrastructure and website information components are also mentioned as important elements in the intention to donate online, and from this point of view, it is consistent with the results of the present study. It was also shown that the perceived risk affects the expectation of effort and the pleasure of helping others and leads to the expectation of performance and social influence. In the results of the studies of Hassanzadeh Sarostani et al. (2017) and Bass and Rushdi (2023), the importance of paying attention to the perceived risk is also mentioned, and from this point of view, it is consistent with the results of the present study. Finally, the results of the research showed that online donation intention is strengthened through trust. This importance has been confirmed in the results of Shahabi-Shojaei et al.'s study (1401).ConclusionThe present study was conducted with the aim of presenting the online donation intention model for the collective financing of charitable institutions in Iran. Based on the results, it was determined that the facilitating conditions of the technical infrastructure, the quality and transparency of the website information, the security of the site and the application, and the protection of privacy affect the perceived risk. It was also shown that the perceived risk affects the expectation of effort and the pleasure of helping others and leads to the expectation of performance and social influence. Finally, the results of the research showed that online donation intention is strengthened through trust.
Management approaches in the field of smart
Fatemeh Mohammadnezhad Chari; Jahanyar Bamdadsoofi; Iman Raeisi Vanani; Maghsoud Amiri
Abstract
The present paper is conducted through exploratory and inductive approach in order to achieve a business model for data marketplaces. The research paper could be considered as a first attempt in the field of data marketplaces and their business models in Iran. The paper is based on two iterative taxonomy ...
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The present paper is conducted through exploratory and inductive approach in order to achieve a business model for data marketplaces. The research paper could be considered as a first attempt in the field of data marketplaces and their business models in Iran. The paper is based on two iterative taxonomy approach that is first introduced by Nickerson et al.(2013).Mixing of a systematic way on current literatures along with structured interviews by some experts who are involved in this area is applied to gain the main objectives.Our results provide the main bloc of the presented archetype with three sub blocs ،attributes and specifications that is titled value proposition. The sub blocs are named value creation، value capture and value delivery.
Introduction
Recently many online data trading platforms have emerged as a new business paradigm to respond to society’s fundamental needs and rights for specific data. On these data marketplaces, service providers buy raw data from device and application owners or collect it from contributors to offer enriched and value-added data to data consumers such as scientists, businesses, etc. The aim of this study is to develop an architecture of business model for data marketplaces in order to get better a understanding of their business logic.
Hence, the research questions are as follows:
1-What are the attributes of construct blocs of the data marketplace business model?
2- What are the specifications of each attribute in any construct bloc of data marketplace business model?
Literature Review
The concept of business model has evolved during recent years by refining its components. There are different types of business model constructs across the literature, from 9 blocs of Osterwalder and Pigneur (2010) to the business model with 3 blocs proposed by “Hautes Etudes Commerciales de Paris” called Odyssey 3.14. The most famous business model construct includes four components (blocs) with “value proposition” as a core component which refers to the benefits that customers receive and why the company is the best choice for them. (Magretta, 2002; Casadesus et al.,2010). The three sub-constructs include “value creation”, “value delivery”, and “value capture” (Teece, 2010). “Value creation” reflects the products and services offered by the company and also the key activities, resources and processes, and partners. “Value delivery” refers to the corporate interactions with the market and “Value capture” concerns the revenue streams and cost structures which make the profit equation.
Methodology:
The present study is conducted through exploratory and inductive approach to achieve an archetype of a business model for data marketplaces. To the best of our knowledge, this research paper could be considered a first attempt in the field of data marketplaces business model design in Iran.
The methodological orientation of this research is based on two iterative taxonomy approaches that is first introduced by Nickerson et., al (2013). Mixing of a systematic way on current works of literature along with structured interviews by some experts who are involved in this area is applied to gain the main objective and answer the research questions. Through this approach, three following steps are taken in a systematic and repetitive manner.
Systematic literature review of 43 scientific documents and their content analysis
Conducting structured interviews with 5 experts
Visiting 4 online data platforms and data marketplaces websites
Results and discussion:
Findings indicate that the data marketplace business model archetype consists of “value proposition” as a main component with 8 attributes including data goods, technological products, infrastructural services, brokery and curation services, operating services, supporting services, the domain of activities, and proprietary forms. The three sub-components’ attributes concerning the data marketplace business model are figured out as follows:
“Value creation” as a sub-construct with six attributes including key partners, key activities, key processes, key products and services, transaction orientations, data sourcing and data origin, and data time -frame.
“Value delivery” as a second sub-component includes five attributes such as data accessibility, output frames, target audiences, trustworthy mechanisms, and privacy preservation mechanisms.
“Value capture” with five attributes including price discovery mechanisms, payment mechanisms, revenue streams, costing mechanisms, and pricing models.
To sum up, these 24 attributes include more than 100 specifications. All of these specifications are profoundly described in detail across the article. Some attributes have more than 8 specifications such as key partners, key activities, or key processes while others have fewer. Most of the specifications are not exclusive, since a particular platform’s attributes may include one or multiple specifications. For example, a particular data platform could have multiple pricing models such as “pay-per-use”, “freemium” or “flat rate”.
Conclusion
Our taxonomy of the data marketplace business model could be extended by four major concerns of data platforms which are data quality evaluation, data pricing mechanisms, secure data trading and truthfulness, and privacy protection mechanisms. Some aspects of the data marketplace business model are inherently contradictory and a trade-off has to be applied between them. For example, European General Data Protection Regulation (GDPR) tries to make a trade-off between data trading transparency and individual privacy protection. Furthermore, participants’ conflicting interests in order to gain a win-win result have to be considered in all online data platform business models. We suggest future researchers in computer science and IT management science, and data scientists extend our archetype by using methods such as text mining techniques and web crawling.
Keywords: Data Marketplace, Business Model, Archetype, Taxonomy.
