• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.109-121
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• 2023

This study aims to address the literature gap by examining the direct relationship between big data analytics capability, marketing innovation, and organizational innovations. Additionally, this study would examine big data analytics capability as the antecedent for both innovation types and how these relationships influence firm performance. The research model is developed based on the integration of resource-based view and knowledge-based view theories. The quantitative method is used as the research methodology for this study. Based on a purposive sampling method, a total of 115 questionnaires were obtained from managers in star-rated hotels located in Malaysia. Partial least square structural equation modeling (PLS-SEM) is utilized for the data analysis. The result shows that big data analytics capability positively affects marketing and organizational innovations. The findings show that big data analytics capability and organizational innovation positively influence firm performance. Nonetheless, the result revealed that marketing innovation is not positively related to firm performance. The findings also indicate to hotel managers the importance of big data analytic capability and the resources required to build and develop this capability. The contributions from this study enrich the literature on big data and innovation, which is particularly limited in the hospitality and tourism context.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.897-911
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• 2021

This study aims to analyze the important role of business analytics capability, information quality, and innovation capability in influencing organization agility and organization performance during the Covid-19 pandemic. Data was collected from 76 companies from various sectors in Indonesia. Structural Equation Model-Partial Least Square (SEM-PLS) analysis was conducted to analyze the relationship between variables and test a series of hypotheses. Importance-Performance Matrix Analysis (IPMA), a useful analysis approach in PLS-SEM, is used, which extends the results of the estimated path coefficient (importance) by adding a dimension that considers the average values of the latent variable scores (performance). The IPMA approach examines not only the performance of an item but also the importance of that item. The results show that business analytics capability has a significant effect on information quality and innovation capability which then affects organization agility. Organizational performance is influenced by organizational agility. IPMA results show that organizational agility has the highest level of impact on organizational performance. This study will assist companies in planning business analytics, improving information quality, increasing innovation capability, and ultimately increasing agility and performance during the Covid-19 pandemic. This study will add to existing knowledge about previous literature, especially in the Covid-19 pandemic situation.

• Smart Media Journal
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• v.10 no.1
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• pp.108-115
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• 2021

This study investigated the relationships between the analytics capability and value of big data and business performance for big data analysts of business organizations. The values that big data can bring were categorized into transactional value, strategic value, transformational value, and informational value, and we attempted to verify whether these values lead to business performance. Two hundred samples from employees with experience in big data analysis were collected and analyzed. The hypotheses were tested with a structural equation model, and the capability of big data analytics was found to have a significant effect on the value and business performance of big data. Among the big data values, transactional value, strategic value, and transformational value had a positive effect on business performance, but the impact of informational value has not been proven. The results of this study are expected to provide useful information to business organizations seeking to achieve business performance using big data.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.207-218
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• 2018

Recently, it becomes a big trend in the banking industry to apply a big data analytics technique to extract essential knowledge from their customer database. Such a trend is based on the capability to analyze the big data with powerful analytics software and recognize the value of big data analysis results. However, there exits still a need for more systematic theory and mechanism about how to adopt a big data analytics approach in the banking industry. Especially, there is no study proposing a practical case study in which big data analytics is successfully accomplished from the marketing perspective. Therefore, this study aims to analyze a target marketing case in the banking industry from the view of big data analytics. Target database is a big data in which about 3.5 million customers and their transaction records have been stored for 3 years. Practical implications are derived from the marketing perspective. We address detailed processes and related field test results. It proved critical for the big data analysts to consider a sense of Veracity and Value, in addition to traditional Big Data's 3V (Volume, Velocity, and Variety), so that more significant business meanings may be extracted from the big data results.

• Journal of Distribution Science
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• v.21 no.1
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• pp.53-63
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• 2023

Purpose: Big Data analytics (BDA) has been recognized to improve firm performance because it can efficiently manage and process large-scale, wide variety, and complex data structures. This study examines the determinants of Big Data analytics adoption toward marketing and financial performance of global logistic companies in Thailand. The research framework is adopted from the technology-organization-environment (TOE) model, including technological factors (relative advantages), organizational factors (technological infrastructure and absorptive capability), environmental factors (industry competition and government support), Big Data analytics adoption, marketing performance, and financial performance. Research design, data, and methodology: A quantitative method is applied by distributing the survey to 450 employees at the manager's level and above. The sampling methods include judgmental, stratified random, and convenience sampling. The data were analyzed by Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The results showed that all factors significantly influence Big Data analytics adoption, except technological infrastructure. In addition, Big Data analytics adoption significantly influences marketing and financial performance. Conversely, marketing performance has no significant influence on financial performance. Conclusions: The findings of this study can contribute to the strategic improvement of firm performance through Big Data analytics adoption in the logistics, distribution, and supply chain industries.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.150-162
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• 2022

This study investigated the impact of the latest developments in big data analytics capabilities (BDAC) on firm performance. The BDAC have the power to innovate existing management practices. Nevertheless, their impact on firm performance has not been fully is not yet fully elucidated. The BDAC relates to the flexibility of infrastructure as well as the skills of management and firm's personnel. Most studies have explored the phenomena from a theoretical perspective or based on factors such as organizational characteristics. However, this study extends the flow of previous research by proposing and testing a model which examines whether organizational exploration, exploitation and market agility mediate the relationship between the BDAC and firm performance. The proposed model was tested using survey data collected from the long-term employees over 10 years in 250 companies. The results analyzed through structural equation modeling show that a strong BDAC can help improve firm performance. An organization's ability to analyze big data affects its exploration and exploitation thereby affecting market agility, and, consequently, firm performance. These results also confirm the powerful mediating role of exploration, exploitation, and market agility in improving insights into big data utilization and improving firm performance.

• Journal of Distribution Science
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• v.20 no.12
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• pp.89-98
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• 2022

Purpose: Due to the rapid growth of e-commerce in Thailand, the operational excellence of distribution service providers has been elevated. Thus, this research investigated the key drivers of operational performance of e-commerce distributors in Thailand. The research contains key variables: the analytics capabilities of an organization, supply chain disruption orientation, innovation capability, and operational performance. Research design, data, and methodology: An online survey is administered to top managers and key personnel (N=425) employed for at least one year in Thailand's top five e-commerce distributors. The sampling methods were conducted using purposive sampling, quota sampling, and convenience sampling. Confirmatory Factor Analysis and Structural Equation Model were applied to analyze and confirm the model's goodness-of-fit and hypothesis testing. Results: The findings reveal that an organization's analytics capabilities significantly affect supply chain disruption orientation and supply chain resilience. Furthermore, operational performance is affected by supply chain disruption, supplier quality management, and innovation capability. Nevertheless, supply chain resilience and digital supply chain have no significant effect on operational performance. Conclusions: The results imply that supply chain digitalization could drive higher operational performance. Distribution businesses are encountering transformation and disruption, which should address the high level of a digital supply chain, innovation, and quality management to maximize their profit margin and delivery service quality.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.54-63
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• 2016

As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.1-13
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• 2020

This study analyzes the key drivers (commitment, integration of big data, green supply chain management, and green human resource practices) of sustainable capabilities and the influence to which these sustainable capabilities impact the banks' environmental and financial performance. Additionally, this study analyzes the impact of green management practices on the integration of big data technology with operations. The theory of dynamic capability was deployed to propose and empirically test the conceptual model. Data was collected through a self-administrated survey questionnaire from 319 participants employed at 35 banks located in six ASEAN countries. The findings indicate that big data analytics strategies have an impact on internal processes and banks' sustainable and financial performance. This study indicates that banks committed towards proper data monitoring of its clients achieve operational efficiency and sustainability goals. Moreover, our results confirm that banks practising green innovation strategies experience better environmental and economic performance as the employees of these banks have received advance green human resource training. Finally, our study found that internal and external green supply chain management practices have a positive impact on banks' environmental and financial performance, which confirms that ASEAN banks contributing in reduction of environmental impact through its operations will ultimately experience increased financial performance.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.321-350
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• 2024

When a competent employee leaves an organization, the technical skills and know-how possessed by that employee also disappear, which may lead to various problems, such as a decrease in organizational morale and technology leakage. To address such problems, it is important to increase employees' job satisfaction. Due to the advancement of both information and communication technology and social media, many former and current employees share information regarding companies in which they have worked or for which they currently work via job portal websites. In this study, a web crawl was used to collect reviews and job satisfaction ratings written by all and incumbent employees working in nine industries from Job Planet, a Korean job portal site. According to this analysis, regardless of the industry in question, organizational culture, welfare support, work system, growth capability and relationships had significant positive effects on job satisfaction, while time and attendance management, performance management, and organizational flexibility had significant negative effects on job satisfaction. With respect to the path difference between former and current employees, time and attendance management and organizational flexibility have greater negative effects on job satisfaction for current employees than for former employees. On the other hand, organizational culture, work system, and relationships had greater positive effects for current employees than for former employees.