PENGARUH ARTIFICIAL INTELLIGENCE TERHADAP CUSTOMER EXPERIENCE (STUDI PADA PENGGUNA GOJEK BANDUNG, JAWA BARAT)

Artificial Intelligence (AI) allows marketers to differentiate their consumers into different personas and understand what motivates consumers in purchasing decisions so that marketers use AI to identify and predict consumer behavior, and through this information marketers are able to build unique experiences for consumers. This study tests and measures the effect of AI on customer experience by using simple regression analysis with four dimensions of AI: mechanical intelligence, analytical intelligence, intuitive intelligence and empathetic intelligence on the four dimensions of customer experience: immersion, flow, cognitive fit and emotional fit with a four-point Likert scale. Based on the results of study on 116 respondents, it it known that both AI and Gojek's customer experience are good and there is a significant effect of AI on customer experience. Artificial Intelligence (AI) memungkinkan pemasar untuk membedakan konsumennya menjadi persona yang berbeda dan memahami apa yang memotivasi konsumen dalam keputusan pembelian sehingga pemasar menggunakan AI untuk mengidentifikasi dan memprediksi perilaku konsumen, dan melalui informasi ini pemasar mampu membangun pengalaman unik bagi konsumen. Penelitian ini bertujuan untuk menguji dan mengukur pengaruh AI terhadap customer experience dengan menggunakan analisis regresi sederhana dengan empat dimensi AI: mechanical intelligence, analytical intelligence, intuitive intelligence dan empathetic intelligence terhadap empat dimensi customer experience: immersion, flow, cognitive fit dan emotional fit dengan skala Likert empat poin. Berdasarkan hasil penelitian terhadap 116 responden, didapatkan bahwa baik AI maupun customer experience Gojek dinilai baik dan terdapat pengaruh yang signifikan AI terhadap customer experience.

A Picture Is Worth a Thousand Journal Entries: Accounting Graph Topology for Auditing and Fraud Detection

Although journal entries are an important component of modern accounting, existing research and practice in auditing and fraud detection have not fully exploited the information made available by the double-entry mechanism. This paper proposes a theory-based methodology, accounting graph topology, to visualize journal entries and explicate within- and between-entry relationships. Grounded in cognitive fit theory and graph theory, this methodology can help auditors identify potential internal control issues and problematic transactions for further investigation. We illustrate the benefits of accounting graph topology by applying it to a case study.

Understanding automated conversational agent as a decision aid: matching agent's conversation with customer's shopping task

PurposeTo provide better services to customers, especially immediate responses and 24/7 availability, businesses are implementing text-based automated conversational agents, i.e. chatbots on their social platforms and websites. Chatbots are required to not only provide customers with necessary consultancy and guidance but also communicate friendly and socially. Based on the cognitive fit theory, this study attempts to examine the role of chatbot as a decision aid and how the match between information presentation in forms of decisional guidance and communication style and the shopping task influences consumers' perceived cognitive fit and decision performance outcomes.Design/methodology/approachA 2 x 2 x 2 between subject online experiment was conducted to identify which kind of decisional guidance (suggestive and informative guidance) and communication style (task-oriented vs social-oriented style) are the most appropriate for each type of shopping task (searching vs browsing task).FindingsThe findings show that when customers interact with chatbots, they will perceive higher cognitive fit if the chatbots provide them with suggestive guidance and communicate in a friendly style especially when they perform a searching task.Originality/valueThis study is the first attempt to understand the role of chatbots as a decision aid to customers using the communicative language. This study also tries to explore the cognitive fit theory in a novel way, and we propose the information presentation in forms of communicative language rather than matrices, tables and graphs.

Calibrating Word-Sized Graphics for Financial Accounting Applications: Evidence and Examples

ABSTRACT We argue that, to be complete, financial reports should provide information to users about all “material” items. It is therefore desirable that all material effects in financial graphs should be visible to users. If small, word-size graphics are calibrated improperly, users may overlook visually small but numerically material effects. Prior literature does not provide adequate design guidance. We test subjects' ability to detect differing absolute and relative size effects in sparklines. We expand the concept of cognitive fit to include minimum relative and absolute visual sizes of key effects. Consistent with the legibility literature, we find accounting student participants' ability to detect patterns and anomalies over certain approximate minimum relative and absolute size thresholds is robust. Two examples illustrate how our findings on approximate relative and absolute size thresholds apply to financial accounting applications. JEL Classifications: M41; M40.