With the ubiquitous adoption of big data, aligning big data with enterprises has become an increasingly critical issue for managers. To address this issue, we conducted a longitudinal case study to analyze big data-business alignment in manufacturing companies (Gree Electric in particular). We collected research data by visiting the company, interviewing corporate executives, and searching authoritative websites We then analyzed the alignment process through a four-step grounded process. Our findings indicate that the enterprise can develop dynamic capabilities through alignment actions that respond to big-data usage scenarios and thus realize the ultimate alignment between the business and big data. Our study conceptualizes this alignment process and provides meaningful implications for big data-business alignment. Based on the findings, we recommend that the enterprise’s alignment actions and dynamic capabilities should evolve in concert to achieve big data-business alignment, and thus offer practical guidance on the use of big data in manufacturing companies.

Abstract Although millions of mobile apps have been published in the app store, the majority are seldom downloaded or used. This phenomenon has intensified the competition among service providers for user loyalty. There were plenty of studies investigating user loyalty in the mobile‐app context; nevertheless, most failed to identify those true loyalty users who embraced attitudinal and behavioral loyalty. To address this research gap, this study aims to understand and predict user loyalty by an extended RFD model. We propose that recency, frequency, and duration are able to reflect behavioral loyalty, while category frequency rate and category duration rate are representations of attitudinal loyalty. Using the actual data collected from a third‐party app, we calculate the weights of each variable through the entropy weight method, evaluate users' loyalty in two dimensions, and classify users into four groups (i.e., true loyalty, latent loyalty, moderate loyalty, and no loyalty). We also conduct a dynamic analysis to investigate how users move across different loyalty conditions. The results indicate that majority of users tend to stay on their initial loyalty conditions. For those who have changed their loyalty conditions, it is found that true loyalty users are more likely to become latent loyalty users.