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This study presents the applicability of a model-based approach for loyalty program forecasting using smartphone app in the digital strategy of the retail industry.

The authors develop a dynamic model with the cyclical structure of customer segments through customer experience. They use time-series data on the number of members of the loyalty program, “Seven Mile Program” and confirm the validity of the approximate calculation of customer segment share, customer segment sales share and aggregate sales performance. The authors present three medium-term forecast scenarios after the launch of a smartphone payment service linked with the loyalty program.

The sum of the two customer segment shares for forecasting (the sum of the quasi-excellent and excellent customer ratios) is about 30% in each scenario, consistent with an essential customer loyalty (true loyalty) share obtained in the existing empirical study.

Digital strategy in the retail industry should focus more on estimating and forecasting average amounts of customer segments and the number of aggregated customers through the digitalization on the customer side than on individual customer journeys and responses.

Multi-scenario evaluation through simulation of dynamic models from a systemic view can be used for decision-making in retailing digital strategies.

This study builds a model that integrates the cyclicality of customer segment transition through customer experiences into a loyalty matrix framework, which is a method that has previously been used in the hospitality industry.

This study aims to provide evidence of how budget officers use non-financial and accrual-based cost information in the budgeting process and how the usage of this information is influenced by financial constraints.

A randomized survey-based field experiment investigating budget officers in 546 Japanese local governments (LGs) was conducted. This allowed us to identify the budget officers' decision-making in the public sector budgeting process by creating and analyzing primary data with regression models.

We found that budget officers suppress budget amounts based on non-financial information of good performances. Under fiscal constraints, officers further reduce budget amounts using information on high accrual-based costs and poor non-financial performance.

Our survey-based field experiment allowed us to obtain primary data from officers making budget decisions. To the best of our knowledge, this study provides the first evidence that non-financial good and poor performance information and accrual-based cost information affect budget officers' decision-making under financial constrain.