REWARD MODEL DEVELOPMENT FOR REFERRAL REWARD PROGRAM IN DELIVERY SERVICES BASED ON CUSTOMER PREFERENCES (CASE PT X)

• Main Author: C Simanullang, Verayanti
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/84699
• Institution: Institut Teknologi Bandung

To support the success of a referral reward program, it is essential to offer rewards that motivate existing customers to refer new ones. These rewards consist of various attributes based on customer preferences, such as reward size, reward scheme, reward type, and reward visibility. Several studies have attempted to understand the impact of these reward attributes on the design of referral reward programs. However, it is not enough to merely examine the influence of individual reward attributes; it is crucial to determine the configuration of reward attributes that align with customer preferences, as customers do not view rewards based on isolated attributes. This research aims to develop an effective reward model for a referral reward program based on a configuration of reward attributes (reward size, reward scheme, reward type, and reward visibility) in delivery services. The study was conducted in two phases: the first phase was to identify the configuration of reward attributes that align with customer preferences, and the second phase was to test the effectiveness of the reward configuration obtained in the first phase to develop a referral reward program that influences perceived attractiveness and metaperception, thereby increasing the likelihood to recommend. In the first phase, conjoint analysis was used with 206 respondents, targeting customers of a state-owned logistics company. The configuration of reward attribute levels obtained from this phase included utilitarian reward for the reward type attribute, the larger reward for the reward size attribute, reward both for the reward scheme attribute and public reward for the reward visibility attribute. The resulting reward configuration was tested for its significance with the likelihood to recommend and its effectiveness in developing a referral reward program that influences perceived attractiveness and metaperception. The results from the reward configuration were used to create scenarios for effectiveness testing. The second phase of the study employed two scenarios: the first scenario used the reward configuration that ranked highest in customer preference, and the second scenario used the configuration that ranked second. This phase involved two groups of respondents, each consisting of 119 individuals. The findings revealed a significant impact of the reward model based on customer preferences on increasing the likelihood to recommend. However, when perceived attractiveness and metaperception mediated the relationship, the referral reward program with a reward configuration of utilitarian reward, larger reward, reward you, and private reward did not significantly enhance the likelihood to recommend. Consequently, the referral reward program that effectively influences perceived attractiveness and v metaperception to boost the likelihood to recommend combines utilitarian reward, larger reward, reward both, and public reward.