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1 – 2 of 2Yan Zhang, Nan Wang and Yongqiang Sun
Technology upgrade has been adopted as a strategy for technology vendors to modify and improve their incumbent technologies. However, user resistance is widespread in practice. In…
Abstract
Purpose
Technology upgrade has been adopted as a strategy for technology vendors to modify and improve their incumbent technologies. However, user resistance is widespread in practice. In order to understand user technology upgrade behavior, this study integrates the retrospective and prospective sides of actions and proposes an inertia-mindfulness ambidexterity perspective to explore the antecedents of technology upgrade.
Design/methodology/approach
An online survey was conducted to collect data from 520 Microsoft Windows users to test this research model. Structural equation modeling (SEM) approach was used to evaluate measurement model and structural model.
Findings
Inertia can induce individuals' psychological reactance and thus reduce their intention to upgrade. In contrast, mindfulness can decrease users' psychological reactance and then motivate them to upgrade to a new version of technology. Finally, individuals' dissatisfaction with the current version of technology would weaken the negative impact of psychological reactance on upgrade intention.
Originality/value
This study generates an inertia-mindfulness ambidexterity perspective to investigate the factors that influence user technology upgrade intention from both retrospective and prospective sides and then identifies psychological reactance as underlying mechanism to explain how inertia and mindfulness work. Finally, this study posits that user dissatisfaction with current version of technology can moderate the relationship between psychological reactance and technology upgrade intention.
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Keywords
Chu-Le Chong, Siti Zaleha Abdul Rasid, Haliyana Khalid and T. Ramayah
This study investigated the relationships among big data analytics capability (BDAC), low-cost advantage, differentiation advantage, market and operational performance…
Abstract
Purpose
This study investigated the relationships among big data analytics capability (BDAC), low-cost advantage, differentiation advantage, market and operational performance underpinning the resource-based view (RBV) and the entanglement view of sociomaterialism (EVS) theories.
Design/methodology/approach
A total of 191 responses from members of the Federation of Malaysian Manufacturers were analysed using a structural equation modelling approach.
Findings
This study has conclusively demonstrated that BDAC is indeed a resource bundle comprising human skills, tangible and intangible resources. This study found that BDAC positively influences competitive advantage and firm performance. The differentiation advantage was found to be a key factor in explaining market performance. Theoretically, both RBV and EVS could be used to link BDAC, differentiation advantage and market performance to explain superior firm performance.
Research limitations/implications
First, the sample is restricted to the manufacturers in Malaysia. Second, a single independent variable, BDAC, is used as a higher-order capability to influence competitive advantage, and thus, superior firm performance. Third, this study uses a self-reported survey, which means that only one respondent from each firm answered the questions. Fourth, this study excludes the focused strategy as it aims to investigate the competitive strategy used in the broader industry environment, rather than in a specific segment pursuing a focused strategy.
Practical implications
First, BDAC is a valuable, rare, inimitable and non-substitutable tool for manufacturers to enhance their firm performance. Second, BDAC is crucial for manufacturing firms to reduce costs and differentiate themselves. Third, a low-cost advantage may not help manufacturers achieve greater market and operational performance.
Originality/value
The relationship among BDAC, low-cost advantage, differentiation advantage, market and operational performance within manufacturing industry is empirically tested.
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