Purpose
Offshoring is a common practice to operationalize global business strategies. Data protection and privacy assurance are major concerns in such international arrangements. This paper aims to examine the strategy adopted to ensure privacy assurance in offshoring arrangements.
Design/methodology/approach
This is a literature review to understand privacy assurance strategies adopted in offshoring arrangements and an exploratory case study of captive offshoring arrangement with onshore location in Canada and offshoring locations in India and Philippines. A comparative analysis of the privacy laws and privacy principles of Canada, Philippines and India has been done.
Findings
It was found that at the time of migration of process or work to the offshore location, organizations follow a conformist privacy strategy; however, once in business as usual mode, they follow entrepreneur privacy strategy. Privacy impact assessment (PIA) was found to be an important element in resolving the “administrative problem” of an offshoring organization’s privacy assurance strategy.
Research limitations/implications
The core privacy principles are outlined in the PIA templates; however, the current templates are designed to meet the conformist strategy and may need to be revised to include the cultural aspects, training, audit and information security requirements to plan and deliver on the entrepreneur strategy.
Practical implications
Offshoring organizations can benefit by planning for entrepreneur privacy assurance strategy at the inception stage. Enhancements to PIA templates to facilitate the same have been suggested.
Originality/value
Privacy assurance strategy followed by organizations while offshoring has been examined. This paper suggests extending the PIA process so that it covers privacy assurance requirements in offshoring arrangements. The learnings can be used in managing privacy assurance requirements in similar multi-country offshore arrangements.
PurposeThe study aimed to examine the robotic process automation (RPA) contextual (center of excellence and scalability) and the multidisciplinary (TOE) determinants of RPA adoption in service industries in the emerging economy.Design/methodology/approachTen factors were identified through literature surveys and popular studies grounded in technology, organization and environment. SPSS AMOS SEM is used for scale measurement and hypotheses testing. A sample of 313 respondents was collected from middle to above middle management executives of service industries from India. The authors tested the hidden layers and non-linear relationships using artificial neural network (ANN) analysis.FindingsThe low complexity, center of excellence (CoE), and industry/business partner pressure were significant to the RPA adoption in service industries in emerging economies. Counterintuitively, the scalability showed a negative influence on the RPA adoption, and the process capability did not show influence. The results of SEM and ANN were consistent.Research limitations/implicationsThis research can unfold the RPA adoption scholarly debate to multiple services industries beyond the telecom sector in emerging economies.Practical implicationsRPA is a disruptive technology on the artificial intelligence (AI) continuum. It has the potential to change the ways of working and enable technology-driven transformation. However, despite having thriving service industries that can benefit from RPA, emerging economies lag in adoption compared to the developed nations.Social implicationsThe RPA and automation can bring transformation to human society. Large economies such as India and China have large-scale demand for services, and the waiting lines are a common issue struggled by society. RPA can address the scalability issues of several services.Originality/valueThis study is among the first to examine technology-organization-environment (TOE) with RPA, including RPA contextual variables such as the CoE and scalability. Literature reports TOE applications on several emerging technologies of Industry 4.0 such as cloud, blockchain, big data and 3 Dimensional Printing (3DP), but no or little reported studies around RPA in services industries in emerging markets.
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