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This chapter advances our understanding of vulnerable consumers by exploring new relationships between resource scarcity and consumer decision-making. Although resource scarcity often prompts individuals to pull back on spending, recent research has shown that it can also increase consumers' motivation to engage in behaviors that fulfill their need for personal control. We extend this stream of research by offering the novel proposition that because resource scarcity motivates the desire for control, activating thoughts about scarcity will increase consumers' interest in products offering self-improvement benefits. We offer initial empirical evidence for when resource scarcity causes consumers to forgo their desire to save by increasing their willingness to pay for products that offer self-improvement benefits. In doing so, this chapter (i) highlights resource scarcity, a state of vulnerability, as an antecedent to the desire for self-improvement, (ii) provides a more nuanced perspective on the motivational underpinnings of resource scarcity and its effects on consumption, and (iii) sheds light on when resource scarcity can increase rather than decrease consumer spending.

Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.