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The previous literature has provided mixed findings regarding whether consumers appreciate or are opposed to algorithms. The primary goal of this paper is to address these inconsistencies by identifying the maximizing tendency as a critical moderating variable. In Study 1, it was found that maximizers, individuals who strive for the best possible outcomes, exhibit greater reactance toward algorithm-recommended choices than satisficers, those who are satisfied with a good-enough option. This increased reactance also resulted in decreased algorithm adoption intention. Study 2 replicated and extended the findings from Study 1 by identifying the moderating role of choice goals. Maximizers are more likely to experience reactance to algorithm-recommended options when the act of choosing itself is intrinsically motivating and meaningful (i.e., autotelic choices) compared to when the decision is merely a means to an end (i.e., instrumental choices). The results of this research contribute to a nuanced understanding of how consumers with different decision-making styles navigate the landscape of choice in the digital age. Furthermore, it offers practical insights for firms that utilize algorithmic recommendations in their businesses.

Adding to prior literature that has examined the relationship between maximization and dissatisfaction, the present research suggests that maximizers, as defined by the original maximization scale, are unhappier decision makers than satisficers because maximizers fail to adequately handle dissonant experiences. Throughout three studies that use different conceptualization and measurement of maximization, we show that maximizers are more vulnerable to negative feedback about one’s choice such that they decrease positivity toward the chosen option to a greater level than satisficers. However, this effect was mainly driven by the decision difficulty factor in the conceptualization of maximization. When decision difficulty was conceptualized as a defining component of maximization (Study 1 and 2), “maximizers” show greater positivity drop in the face of negative feedback. However, in the absence of a decision difficulty component, a recently proposed two-component model of maximization (the goal to get the best and search for alternatives; Cheek & Schwartz, 2016) did not play a significant role in predicting positivity drop, while perceived decision difficulty did (Study 3). Together our findings suggest that previously reported contradictory outcomes of maximization may be due to inconsistent conceptualization and measurement, especially treating decision difficulty as a defining component of maximization.

Background: Milbemycins, produced from Streptomyces hygroscopicus subsp. aureolacrimosus and Streptomyces bingchenggensis, are 16-membered macrolides that share structural similarity with avermectin produced from Streptomyces avermitilis. Milbemycins possess strong acaricidal, insecticidal, and anthelmintic activities but low toxicity. Due to the high commercial value of the milbemycins and increasing resistance to the avermectins and their derivatives, it is imperative to develop an efficient combinatorial biosynthesis system exploiting an overproduction host strain to produce the milbemycins and novel analogs in large quantities. Results: The respective replacement of AveA1 and AveA3 (or module 7 in AveA3) of the avermectin polyketide synthase (PKS) in the avermectin high-producing strain S. avermitilis SA-01 with MilA1 and MilA3 (or module 7 in MilA3) of the milbemycin PKS resulted in the production of milbemycins A3, A4, and D in small amounts and their respective C5-O-methylated congener milbemycins B2, B3, and G as major products with total titers of approximately 292 mg/l. Subsequent inactivation of the C5-O-methyltransferase AveD led to a production of milbemycins A3/A4 (the main components of the commercial product milbemectin) in approximately 225 and 377 mg/l in the flask and 5 l fermenter culture, respectively, along with trace amounts of milbemycin D. Conclusions: We demonstrated that milbemycin biosynthesis can be engineered in the avermectin-producing S. avermitilis by combinatorial biosynthesis with only a slight decrease in its production level. Application of a similar strategy utilizing higher producing industrial strains will provide a more efficient combinatorial biosynthesis system based on S. avermitilis for further enhanced production of the milbemycins and their novel analogs with improved insecticidal potential. [ABSTRACT FROM AUTHOR]