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This chapter aims to record the potency of Indonesian culinary products as a part of the tourism ecosystem for key regions in the country. In achieving its objectives, a thorough description and a model were used to explain the phenomenon in different locations. Ten culinary destinations which represent six areas of Indonesia were examined. The observed variables included the type of culinary offering, its history or storytelling behind its involvement, and the location of the production. It was found that the concept of building a supportive tourism ecosystem for development has only been applied in some areas. Except for Bali and Jogjakarta, other destinations are not yet ready to support culinary tourism developments.

In this chapter, the analysis-action nexus will be addressed by reviewing studies from across the chapters. The broad themes covered have included governance and sustainability, consumer benefits and experiences, and networking among entrepreneurs and associated opportunities. It is argued that addition to tourism intelligence often lies in improving communication, building trusted connections, and affirming local networks. A focus on the need for trust, the use of tools for insights, the value of identifying new trends, and understanding target markets, together with the usefulness of training and an appreciation of time, help smooth the journey along the long and winding road from analysis to action.

Auditors’ skills and knowledge of data analytics and big data can influence their judgment at the audit planning stage. At this stage, the auditor will determine the level of audit risk and estimate how long the audit will take. This study aims to test whether big data and data analytics affect auditors’ judgment by adopting the cognitive fit theory.

This was an experimental study involving 109 accounting students as participants. The 2 × 2 factorial design between subjects in a laboratory setting was applied to test the hypothesis.

First, this study supports the proposed hypothesis that participants who are provided with visual analytics information will rate audit risk lower than text analytics. Second, participants who receive information on unstructured data types will assess audit risk (audit hours) higher (longer) than those receiving structured data types. In addition, those who receive information from visual analytics results have a higher level of reliance than those receiving text analytics.

This research has implications for external and internal auditors to improve their skills and knowledge of data analytics and big data to make better judgments, especially when the auditor is planning the audit.

Previous studies have examined the effect of data analytics (predictive vs anomaly) and big data (financial vs non-financial) on auditor judgment, whereas this study examined data analytics (visual vs text analytics) and big data (structured and unstructured), which were not tested in previous studies.