Shahriar Sajib

Dr. Shahriar Sajib


Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 October 2021 in International Journal of Information Management
Verified
Journal Article
Closed access
SAShahriar Akter
Shahriar Akter
GMGrace McCarthy
Grace McCarthy
SSShahriar Sajib
Shahriar Sajib
KMKatina Michael
Katina Michael
YDYogesh K. Dwivedi
Yogesh K. Dwivedi
KSK.N. Shen
K.N. Shen

16 May 2023 in Technovation
Verified
Journal Article
Closed access
SAShahriar Akter
Shahriar Akter
AHAfnan Hossain
Afnan Hossain
SSShahriar Sajib
Shahriar Sajib
SSSaida Sultana
Saida Sultana
Mahfuzur RahmanMahfuzur Rahman
Mahfuzur Rahman
GMGrace McCarthy
Grace McCarthy

2 August 2022 in Annals of Operations Research
Verified
Journal Article
Open access
PDF file
SAShahriar Akter
Shahriar Akter
MUMohammad Rajib Uddin
Mohammad Rajib Uddin
SSShahriar Sajib
Shahriar Sajib
WLWai Jin Thomas Lee
Wai Jin Thomas Lee
KMKatina Michael
Katina Michael
Mohammad Alamgir HossainMohammad Alamgir Hossain
Mohammad Alamgir Hossain
Data breaches have become a formidable challenge for business operations in the twenty-first century. The emergence of big data in the ever-growing digital economy has created the necessity to secure critical organizational information. The lack of cybersecurity awareness exposes organizations to potential cyber threats. Thus, this research aims to identify the various dimensions of cybersecurity awareness capabilities. Drawing on the dynamic capabilities framework, the findings of the study show personnel (knowledge, attitude and learning), management (training, culture and strategic orientation) and infrastructure capabilities (technology and data governance) as thematic dimensions to tackle cybersecurity awareness challenges.

20 September 2022 in Annals of Operations Research
Verified
Journal Article
Open access
PDF file
UHUmme Hani
Umme Hani
Ananda WickramasingheAnanda Wickramasinghe
Ananda Wickramasinghe
Uraiporn KattiyapornpongUraiporn Kattiyapornpong
Uraiporn Kattiyapornpong
SSShahriar Sajib
Shahriar Sajib
Data-driven innovation (DDI) initiatives by microfinance institutes have transformed the global poverty alleviation landscape. Despite the fact that relationship building is one of the primary goals of DDI initiatives in microfinance operations, there has been little research on the dimensions of relationship quality. This study examines how DDI initiatives recognize and incorporate relational dimensions in their service offerings to alleviate poverty. Drawing on a systematic literature review, thematic analysis and interviews with 20 microfinance managers, this research explores the relationship quality parameters that need to be leveraged. Grounded in the resource-based theory, the findings of this study confirm trust and commitment as two key relationship capabilities. The findings contribute to a better understanding of how microfinance institutes can use DDI to achieve sustainable competitive advantage.

16 November 2021 in Annals of Operations Research
Verified
Journal Article
Closed access
PDF file
SAShahriar Akter
Shahriar Akter
SMSaradhi Motamarri
Saradhi Motamarri
SSShahriar Sajib
Shahriar Sajib
RBRuwan J. Bandara
Ruwan J. Bandara
STShlomo Tarba
Shlomo Tarba
Demetris VrontisDemetris Vrontis
Demetris Vrontis

20 June 2023
Verified
Book Chapter
Closed access
PDF file
SAShahriar Akter
Shahriar Akter
Samuel FOSSO WAMBASamuel FOSSO WAMBA
Samuel FOSSO WAMBA
SSShahriar Sajib
Shahriar Sajib
SHSahadat Hossain
Sahadat Hossain
Big Data Analytics (BDA) has been emerging as enabler to the organizations in various industries to their efforts ranging from innovations of products to decision making. Small and medium organizations usually replicate the large organizations' BDA efforts to succeed, but effective operationalization of BDA to those contexts largely remain unanswered in the literature. Integrating the previous literatures, the authors offer a Six Steps BDA research framework in aid to context specific data driven decision making. These steps include problem recognition, review of past findings, variables selection & model development, collecting data & testing the model, data analysis, and insights-based actions. The steps in this generic sequential framework are co-influencing, which at any stage can be reversed to the previous stage resulting in correct and strengthen the whole model. To ensure the best use of this linear model, organizations require to understand the systematic approach and execution strategies of BDA research. To avoid the inherent challenges of Big Data like; abundance, heterogeneity, incompleteness, inconsistency etc., practitioners and scholars should remain focused on acquisition, storage, and processing of data. Organizations need to calibrate their talent capabilities reflecting the evolving expectations of data-driven decision making for ongoing dynamic environmental circumstances.