Loan-to-value policy in a bubble-creation economy

This paper provides a theoretical analysis of macroprudential policy in the form of loan-to-value (LTV) restrictions in a bubble-creation economy. Such policy measures have been used intensively in Asia to deal with credit and property price boom-bust cycles while the effectiveness remains unclear, especially the country-specific cases. We find that the effect on investment and size of bubbles depends on the degree of financial development. Specifically, restrictive LTV policies tend to be more effective in dampening asset-price bubbles in economies that have a high degree of financial depth. High (low) financial depth implies that bubbles originally crowd out (in) investment, so that implementation of LTV policies causes bubbles to decrease (remain unchanged). These might explain some mixed empirical results of LTV policy on dampening asset-price growth.     

(Im)Balanced customer-oriented behaviors and AI chatbots' Efficiency–Flexibility performance: The moderating role of customers’ rational choices

Artificial intelligence (AI) based chatbots are increasingly deployed in frontline encounters, because they combine frontline service efficiency and flexibility. Using a large-scale data set with more than 130,000 man–machine dialogues from an e-bike sharing platform, Study 1 reveals a complex relationship between chatbots' customer-oriented behaviors and their efficiency–flexibility ambidexterity. Chatbots' level of efficiency–flexibility ambidexterity is higher when their functional and relational customer-oriented behaviors are balanced rather than imbalanced (i.e., a negative imbalance effect) and when they are balanced at a higher rather than a lower level (i.e., a positive balance effect). A follow-up experiment, Study 2, and online survey, Study 3, consistently show that the negative imbalance effect is stronger as customers' perceptions of non-personalization costs decrease and privacy concerns increase, while opportunity cost has no significant influence on the negative imbalance effect. However, consistent with rational choice theory, the positive balance effect is stronger as non-personalization costs increase, privacy concerns decrease, and opportunity cost decreases. In addition, Study 1 and 3 consistently show that in alignment with the stimulus–organism–response framework, efficiency–flexibility ambidexterity partially mediates the relationship between chatbots’ (im)balanced customer-oriented behaviors and customer patronage. This study contributes to the literature on frontline ambidexterity by introducing an AI application context and a more nuanced nonlinear view of the antecedents and consequences of frontline ambidexterity.     

Reference-dependent preferences and probability judgments

This paper provides empirical evidence that probability judgments help explain a reference-dependent preference. It explains using the data for Tokyo Taxi drivers, which includes the respondents about psychological questions. Probability judgments based on a dual process to cognition means judgments and calculations of the probability when they determine something under uncertainty. It weakens the assumption that people have the same rationality. We permit the difference between cognition to rationality and probability judgments. These probability judgments relate to reference points and drivers' personalities and intuition, which influence decision-making and can explain several reference dependences. The difference of cognition to rationality also determines their reference dependence type. It uses Rational Experienced Inventory as the index of cognition to rationality. Frequentist type has different target variables with case type, subjective type, and fortune type. Each probability judgment type has each target as the reference point. Probability judgment types explain the type of cognition to rationality. This explains the endogeneity of reference dependence.