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A mobile office can be defined as software and hardware that support business work and that are accessible via smartphones. Organizations are adopting or trying to adopt the mobile office as their communication and business tool, in order to support or to change their current work environments. The purpose of this paper is to focus on the impact of outsourcing strategy on mobile office performance based on the FORT model and the MoBiS-Q to measure the performance of the outsourced mobile office.

This study focuses on the impact of outsourcing strategy on mobile office performance based on the FORT model and the MoBiS-Q to measure the performance of the outsourced mobile office.

In the results, the perceived usability and the perceived impact of the mobile office on the productivity can be higher with the Alliance outsourcing relationship type. The outsourcing of the mobile office would follow the similar process of other IT outsourcing strategies. But the device fittingness was far from the expectation.

Firms/organizations should consider a more concrete step-by-step approach to strategic outsourcing relationships with the high-skilled expert groups and that the device fittingness may not be a proper factor to measure the performance of the outsourced mobile office.

IT managers who plan or now try to adopt the mobile office into their organizations should focus on developing proper applications and software; however, they do not need to care about device or H/W issues, including employees ' skills at using the devices or the device fittingness for the planned mobile office.

This study is one of the first academic studies to analyze the impact of outsourcing strategy of the mobile office on organization performance.

The purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.

In order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.

First, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.

This research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.

The proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.

The proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.

The key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

Prodemocracy protest in South Korea in the 1980s can be described in terms of two waves of sustained activism between 1979 and 1987. One wave was brutally repressed in the Gwangju Uprising of May 1980, while the other succeeded in bringing in a transition to democracy in June 1987. How did activists recover from the repression in the first wave, and how did they create a viable movement in the second wave? This work focuses on the role of memory work about the Gwangju Uprising in the mobilization of the prodemocracy movement. Drawing on a wide assortment of documents collected from various archives in South Korea, the author demonstrates how memory work contributed to the movement dynamics. Cognitively, memory work radicalized movement participants such that they became completely disillusioned with the legitimacy of state power. Emotionally, memory work triggered a moral shock among recruits that motivated them to take the high risks associated with activism. Relationally, memory work provided a bonding experience for activists within a network. The findings also show a process through which memory work becomes a powerful social force: emergence of a challenger, proliferation of an alternative narrative, and then a full-blown contention between the state and a challenger. The process also means changes of the status of memory in terms of ownership, salience, and valence.