Bloatware refers to software that contains unwanted plug-ins. Jailbreak software aims to eliminate these undesirable components and enhance the flexibility of bloatware, while custom software is specifically tailored to fulfill individual customer requirements. In contrast to previous research, our study addresses the challenge of proposing two strategies to mitigate bloatware inadaptability: jailbreak and customization. Some interesting conclusions were drawn. Firstly, when customization costs are low and the degree of customization is high, users tend to purchase bloatware at a lower price rather than opting for the expected high-priced customized software, indicating that consumers do not prefer the high premiums associated with extensive customization. Moreover, when users’ effort and the positive benefits of jailbreaking are low, they surprisingly choose to pay a higher price for jailbroken software. This phenomenon challenges the conventional view that jailbreaking is typically regarded as a low-cost alternative sought by users. Secondly, due to the larger user base in the jailbreak market compared to the bloatware market, there exists a market vacuum that can be capitalized on by jailbreak software vendors. It is worth noting that the profit margins in the jailbreak market tend to be higher than those in the bloatware market, indicating the disruptive influence of small firms resembling jailbreak vendors on the software industry. Despite the presence of established bloatware companies, jailbreak vendors still manage to capture a significant share of the overall profits. Thirdly, our research findings indicate that user jailbreak behavior leads to the highest consumer surplus. Finally, we conducted a comprehensive case study analyzing the impact of different user behavior strategies on software enterprises’ revenue in the context of bloatware, customized, and jailbroken apps specific to Apple’s platform.

While the development of e-commerce brings convenience to consumers, a large quantity of products and information increase the difficulty of making purchase decisions. This study constructs a classification-based product selection method driven by online reviews to assist consumers in making purchase decisions. First, the multifaceted attribute evaluations of products are extracted from textual reviews that contain more abundant and useful information than those provided by vendors. The evaluations are modeled by probabilistic linguistic term sets such that sentiment words in texts are described at different frequencies. Then, a classification-based product selection method is developed to rank products considering multifaceted attributes in which alternative products are divided into the acceptance class, rejection class, and uncertainty class through a classification strategy. Each class of products is compared based on the performance scores calculated by a probabilistic linguistic aggregation operator. A case study of selecting laptops based on real data from Amazon.com is given to illustrate the method. Comparative analysis with existing ranking methods shows the advantages of the proposed method in matching consumers’ risk aversion behavior and preserving uncertain information.