IoT big data value map : how to generate value from IoT data

Huge sources of business value are emerging due to big data generated by the Internet of Things (IoT) technologies paired with Machine Learning (ML) and Data Mining (DM) techniques' ability to harness and extract hidden knowledge from data and consequently learning and improving spontaneously. This paper reviews different examples of analyzing big data generated through IoT in previous studies and in various domains; then claims their business Value Proposition Map deploying Value Proposition Canvas as a novel conceptual tool. As a result, the proposed unprecedented framework of this paper entitled "IoT Big Data Value Map" shows a roadmap from raw data to real-world business value creation, blossomed out of a kind of three-pillar structure: IoT, Data Mining, and Value Proposition Map. The result of this study paves the way for prototyping business models in this field based on value invention from huge data analysis generated by IoT devices in different industries. Furthermore, researchers may complete this map by associating proposed framework with potential customers' profile and their expectations

Modelling the asymmetrical relationships between digitalisation and sustainable competitiveness : a cross-country configurational analysis

Sustainable competitiveness (SC) encourages nations not only to meet the needs of the current generation but also to sustain or even expand national wealth in the future without depleting natural and social capital. Drawing on complexity theory, we used a configurational approach to identify under what necessary and sufficient conditions, digitalisation contributes to achieve higher SC. Shifting attention from net effects to configuration analysis improves our understanding of cross-national differences in sustainability by exploring how the digitalisation factors combine to strengthen SC power across countries. To address the complexity of this configuration, we have incorporated fsQCA and NCA techniques in the modelling of high and low levels of sustainable competitiveness recipes. Analysis of data from 127 countries advanced our perception of how access to ICT infrastructures and capabilities, combined with the adoption and usage of ICT could result in different degrees of sustainable competitiveness. Theoretically, this study contributes to the literature on digitalisation and national sustainability; and it can practically act as a guideline for policymakers to understand the complex interactions and causal configurations of digitalisation factors on sustainability

Elucidation of big data analytics in banking : a four-stage Delphi study

Purpose In today's networked business environment, a huge amount of data is being generated and processed in different industries, which banking is amongst the most important ones. The aim of this study is to understand and prioritize strategic applications, main drivers, and key challenges of implementing big data analytics in banks. Design/methodology/approach To take advantage of experts' viewpoints, the authors designed and implemented a four-round Delphi study. Totally, 25 eligible experts have contributed to this survey in collecting and analyzing the data. Findings The results revealed that the most important applications of big data in banks are “fraud detection” and “credit risk analysis.” The main drivers to start big data endeavors are “decision-making enhancement” and “new product/service development,” and finally the focal challenge threatening the efforts and expected outputs is “information silos and unintegrated data.” Originality/value In addition to stepping forward in the literature, the findings advance our understanding of the main managerial issues of big data in a dynamic business environment, by proposing effective further actions for both scholars and decision-makers

Applying data mining method for marketing purpose in social networks: case of Tebyan

Within a very short period of time, social networking sites are developed among different users all around the world. Social networks have high value to business intelligence. In these networks, there are so many advantages and demands on addressees and their interest recognition. How do we increase our social network users, posts, and effectiveness? How many consumers can be segmented with respect to their reactions to social network? The creation of a target market strategy is integral to developing an effective business strategy. The purpose of this article is market segmentation and correctly identifying the target groups for social network using data mining techniques. As users in each segment have their own and specific interests, social networks can define them by their demographic profiles, they can also change their development strategies according to users and interests they want to engage in. In this research, we deploy data mining methods for segmenting Tebyan social network users to see how this method could contribute toward marketing strategies and purposes. According to K-mean algorithm, we demonstrate five different customer categories based on their characteristics and behaviour that deploying appropriate strategy for each category can help the marketing performance

A person‐centred view of citizen participation in civic crowdfunding platforms: A mixed‐methods study of civic backers

AbstractCrowdfunding platforms have emerged as a promising contemporary means for mobilising collective civic actions to address local or social issues, improve community cohesion and develop the public good. This empirical study taps into the understudied civic crowdfunding platforms (CCP) developed to facilitate such actions, proposing, supporting and funding public‐interest projects through crowdsourcing and microfinancing. Previous studies have shown that individuals' characteristics affect their level of civic engagement with social issues. Considering the diversity of contributor motivations, we aim to shed light on the dynamics of emergent subpopulations of citizens who participate in CCPs. To this end, we use a sequential mixed‐methods approach to integrate our fuzzy set Qualitative Comparative Analysis (fsQCA) findings with the results of an in‐depth qualitative study, to gain rich and robust inferences and meta‐inferences. In Study 1 (n = 316), we used fsQCA to explore five distinctive configural profiles that display the heterogeneity of civic backers' motivations, including civic champions, prosocial advocates, normative supporters, reward seekers and regret‐averse contributors. In Study 2, we corroborated and complemented our fsQCA inferences through an extreme‐case study and identified four boundary conditions. Taken together, our inferences and meta‐inferences address the heterogeneity of motivations for participating in CCPs, by understanding and theorising about diverse profiles of citizen backers. Finally, we offer practical implications for successful civic crowdfunding initiatives.</jats:p

Exploring the paths to big data analytics implementation success in banking and financial service: an integrated approach

Purpose Big data analytics (BDA) is recognized as a recent breakthrough technology with potential business impact, however, the roadmap for its successful implementation and the path to exploiting its essential value remains unclear. This study aims to provide a deeper understanding of the enablers facilitating BDA implementation in the banking and financial service sector from the perspective of interdependencies and interrelations. Design/methodology/approach We use an integrated approach that incorporates Delphi study, interpretive structural modelling (ISM) and fuzzy MICMAC methodology to identify the interactions among enablers that determine the success of BDA implementation. Our integrated approach utilizes experts' domain knowledge and gains a novel insight into the underlying causal relations associated with enablers, linguistic evaluation of the mutual impacts among variables and incorporating two innovative ways for visualizing the results. Findings Our findings highlight the key role of enabling factors, including technical and skilled workforce, financial support, infrastructure readiness and selecting appropriate big data technologies, that have significant driving impacts on other enablers in a hierarchical model. The results provide reliable, robust and easy to understand insights about the dynamics of BDA implementation in banking and financial service as a whole system while demonstrating potential influences of all interconnected influential factors. Originality/value This study explores the key enablers leading to successful BDA implementation in the banking and financial service sector. More importantly, it reveals the interrelationships of factors by calculating driving and dependence degrees. This exploration provides managers with a clear strategic path towards effective BDA implementation

Proposing a basic methodology for developing balanced scorecard by system dynamics approach

Purpose: Successful future has inspired organizations to measure long-term and non-financial measurements and key performance indicators (KPIs). Kaplan and Norton proposed balanced scorecard (BSC) for this issue and have extended it to one of the most preferred strategic management system’s tools. However, available planning tools like BSC have some limitations, like dependency to the developer, weakness in showing time delays, and also mathematical relationships between lead and lag indicators. In this paper, the authors would present a new methodology for developing BSCs, which would be able to overcome these limitations. Therefore, the purpose of this paper is to develop an integrated framework for developing BSC with system dynamics approach (a dynamic BSC (DBSC)) which has lower limitation in compare with traditional BSC. The other purpose of this paper is developing a DBSC for an Iranian public transportation company. Design/methodology/approach: Based on this purpose, related literature was thoroughly reviewed and the proposed methodology designed using the system dynamics and BSC concepts. This methodology is a composition of original BSC development methodology and system dynamics principles. An assumed organization has been used for showing methodology’s capability and procedure. Furthermore, a case study has been accomplished in this paper. This case study is a DBSC which has been developed for an Iranian public transportation company. The purpose of this case study is to ensure about proposed methodology implication in action. Findings: The authors proposed a methodology which can be applied for developing BSCs. This methodology consists of six different steps which are: developing a system for organization, selecting stakeholders’ most important objectives and target, identifying organization’s objectives and their KPIs for different BSC aspects, developing strategy map, targeting, and selecting initiatives. In the proceeding of this paper, the proposed methodology and its steps would be explained in detail. Originality/value: The system dynamic approach has precedents in business studies; however, this research makes this approach operational in BSC designing and analysis. BSCs, which developed by this methodology can show time delays between an organization’s objectives, its KPIs’ relationship and also planning for it. Selecting achievable and rational vision and objectives’ targets, change management, scenario planning and policy analysis are other values which can be achieved by DBSC deployment which need further researches. In summary, this research has shown an integrated framework for developing DBSC and then applies it to an Iranian public transportation company. Therefore, another contribution of this paper is the application of this method for an Iranian public transportation company

Citizens' support in social mission platforms: Unravelling configurations for participating in civic crowdfunding platforms

Crowdfunding platforms have recently expanded their mission and domain into promoting social movements such as helping the communities or improving local amenities. This study taps into the less studied type of crowdfunding platforms and seeks to advance our understanding of users' participation in supporting local projects mediated by digital platforms. To this aim, we utilized a theoretical multiplicity approach for developing a configurational theoretical framework that integrates two dominant behavioural theories (VBN and TPB) to empirically test citizens' motives for participating in Civic Crowdfunding Platforms (CCPs). By using fuzzy set qualitative comparative analysis (fsQCA) for examining 537 respondents' data, this study explores the emergent configurations that explain the heterogeneity of citizens' behaviour in CCPs. Following a theoretical multiplicity approach and configurational multiplicity perspective, we explore the complex and asymmetrical interactions between substantive factors shaping different configurations that explain citizens' participation or ∼ participation in CCPs. Analysis of empirical data revealed seven different configurations that can adequately explain participation behaviour and five solutions culminate in ∼participation, among which two exemplify free-rider citizens. This empirical study by bridging the body of knowledge on configurational perspectives with behavioural theories contributes to our understanding of citizen participation in CCPs