Leveraging big data technology for small and medium-sized enterprises (SMEs)

Kalan, Reza Shokri; Ünalır, Murat Osman

doi:10.1109/iccke.2016.7802106

Cited by 15 publications

(24 citation statements)

References 14 publications

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“…Jambunathan and Venkatesan (2016) noted that the possibility of misuse of personal data particulars was likely to occur in big data analysis. Due to data protection and security issues, the adoption of big data especially in funding agencies, Ardagna et al (2016), Vajjhala and Ramollari (2016), Kızıltan (2018), Kalan and Unalir (2016), Karim et al (2017), Sivarajah et al (2017), and Makhele (2018.…”

Section: Legal Perspectives:concerns About Data Protection and Data Privacymentioning

confidence: 99%

“…Architecture Coleman et al (2016), Shah et al (2017), Del Vecchio et al (2018, Soroka et al (2017), Ardagna et al (2016), Vajjhala and Ramollari (2016), Kızıltan (2018), Moyne and Iskandar (2017), Kalan and Unalir (2016), Karim et al (2017), Mbassegue et al (2016), Seseni and Mbohwa (2018), Sivarajah et al (2017), Gupta et al (2018), Limpeeticharoenchot et al (2020), Makhele (2018), andRajabion (2018). Coleman et al (2016), Shah et al (2017, Iqbal et al (2018), Ardagna et al (2016), Kızıltan (2018), Kalan and Unalir (2016), Karim et al (2017), Mbassegue et al (2016), Sivarajah et al (2017, Baharuden et al (2019), Limpeeticharoenchot et al (2020), andMakhele (2018). Coleman et al (2016), Shah et al (2017, Iqbal et al (2018), Soroka et al (2017), Kızıltan (2018), Kalan and Unalir (2016), Limpeeticharoenchot et al (2020), Makhele (2018), andNemati andKhajeheian (2018).…”

Section: Legal Perspectives:concerns About Data Protection and Data Privacymentioning

confidence: 99%

“…Coleman et al (2016), Shah et al (2017, Iqbal et al (2018), Ardagna et al (2016), Kızıltan (2018), Kalan and Unalir (2016), Karim et al (2017), Mbassegue et al (2016), Sivarajah et al (2017, Baharuden et al (2019), Limpeeticharoenchot et al (2020), andMakhele (2018). Coleman et al (2016), Shah et al (2017, Iqbal et al (2018), Soroka et al (2017), Kızıltan (2018), Kalan and Unalir (2016), Limpeeticharoenchot et al (2020), Makhele (2018), andNemati andKhajeheian (2018). academic, and medical sectors is much lower (Mardis, 2016).…”

Section: Legal Perspectives:concerns About Data Protection and Data Privacymentioning

confidence: 99%

“…Jagadish et al (2014) mentioned that the main challenge for SMEs is the ability to respond to complex queries with vast columns of data. Kalan and Unalir (2016) stressed that it is difficult for SMEs to overcome this problem due to their low budget and limited number of employees in their organisation. Marx (2013) also noted that the flow of information was slow due to the decentralisation of the data storage centres for the cloud systems.…”

Section: Architecture Perspectives: Lack Of Infrastructure To Process High Volume Data Efficientlymentioning

confidence: 99%

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Challenges of big data adoption in Malaysia SMEs based on Lessig’s modalities: A systematic review

Chuah

Ramayah

2021

Cogent Business & Management

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This study aims to review the challenges of implementing big data analytics by Small and Medium Enterprises (SMES) in Malaysia. There is a lack of literature studies examining the relationship between Lessig's four modalities and the challenges of big data analytics adoption. This study assesses the implementation of Lessig's Four Modalities for the adoption of big data analytics in Malaysia SMEs based on literature review findings. To identify the challenges of applying big data analytics in Malaysian SMEs, a comprehensive literature search was conducted using databases, journals, Google Scholar, and IEEE publications. This research provides practical insights into the challenges of dealing with big data in SMEs. It is envisioned that Lessig's four modalities proposed in this study, namely legal, architecture, social, and market factors, can be applied by Malaysian SMEs to adopt big data analytics.

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Section: Legal Perspectives:concerns About Data Protection and Data Privacymentioning

confidence: 99%

Section: Legal Perspectives:concerns About Data Protection and Data Privacymentioning

confidence: 99%

Section: Legal Perspectives:concerns About Data Protection and Data Privacymentioning

confidence: 99%

Section: Architecture Perspectives: Lack Of Infrastructure To Process High Volume Data Efficientlymentioning

confidence: 99%

See 2 more Smart Citations

Challenges of big data adoption in Malaysia SMEs based on Lessig’s modalities: A systematic review

Chuah

Ramayah

2021

Cogent Business & Management

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“…Thailand comprises about 3 million SMEs, which generate 43% of the country's GDP, as of 2019. Many studies have been conducted to determine the factors affecting the successful implementation of BDA in SMEs [5][6][7]. Big Data maturity models (BDMMs) have been developed by multiple technology companies, researchers, and consulting firms.…”

Section: Introductionmentioning

confidence: 99%

Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand

Limpeeticharoenchot

Cooharojananone

Chanvanakul

et al. 2020

Int. J. Interact. Mob. Technol.

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A Big Data maturity model (BDMM) is one of the key tools for Big Data assessment and monitoring, and a guideline for maximizing the usage and opportunity of Big Data in organizations. The development of a BDMM for SMEs is a new concept and is challenging in terms of development, application, and adoption. This article aims to create the novel online adaptive BDMM via responsive web application for SMEs. We develop the BDMM API and a responsive web application for easy access via mobile phone. We developed a model by analyzing the factors impacting the success of implementing Big Data Analytics (BDA) in SMEs based on literature reviews. The model was verified by conducting a survey of 180 SMEs in Thailand, interviewed against four extracted domains. Then, the scoring and classified levels for the model was developed through Latent Class Analysis (LCA) to depict four levels of each domain and four final maturity levels to create an adaptive model. As the experimental results with 33 users including executive officers, managers, IT and data analytic officers .The user acceptance for our mobile application using TAM indicates that executive officers group and non-executive group satisfied perceived usefulness, perceived ease of use, and intention to use factor. Use cases of the application include SMEs monitoring for their Big Data Analytics capability for improvement, and the Government Agency providing proper support on SMEs’ level of competency.

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