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Indian shopper motivation to use artificial intelligence: Generating Vroom's expectancy theory of motivation using grounded theory approach

机译:印度购物者使用人工智能的动机:使用扎根理论方法生成Vroom的动机期望理论

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Purpose The purpose of this paper is to understand motivation of young consumers to use artificial intelligence (AI) tools such as chatbots, voice assistants and augmented reality in shopping by generating Vroom's expectancy theory of motivation using grounded theory approach.Design/methodology/approach Grounded theory approach has been used to develop the Vroom's expectancy theory. Initially data were collected through participant interviews using theoretical sampling. These data were analyzed and coded using the three step process, i.e. open coding, axial coding and selective coding. The categories created during coding were integrated to generate Vroom's expectancy theory of motivation.Findings The findings indicate that Vroom's expectancy theory of motivation can be used to explain motivation of young consumers to use AI tools as an aid in taking shopping decisions. The motivation may be intrinsic motivation, extrinsic motivation or force choice motivation. Expectancy represents the ease of using the tools, instrumentality represents competence of tools in performing desired tasks while valence represents satisfaction, rewarding experience and trust in using of tools.Research limitations/implications The findings of the study are based on grounded theory approach which is an inductive approach. Alternate research methodologies, both inductive and deductive, need to be employed to strengthen the external validity and generalize the results. The study is limited to shopping motives of young consumers in India. A comparison with other consumer motivational studies has not been done. Hence no claim is made regarding the advantage of Vroom's theory over other motivational theories.Practical implications The study has strong implications for retailers in developing countries which are seen as an emerging market for retail and have introduced AI tools in recent years. The Vroom's expectancy theory will help retailers to understand consumer motivation in using AI tools or shopping.Originality/value Vroom's expectancy theory to understand consumer motivation to use AI tools in shopping was generated using the grounded theory approach.
机译:目的本文的目的是通过使用扎根理论方法生成Vroom的动机期望理论,以了解年轻消费者在购物中使用聊天机器人,语音助手和增强现实等人工智能(AI)工具的动机。设计/方法/方法扎根理论方法已用于发展Vroom的期望理论。最初,数据是通过使用理论抽样的参与者访谈收集的。使用开放编码,轴向编码和选择性编码这三个步骤对这些数据进行分析和编码。整合了在编码过程中创建的类别,以生成Vroom的动机期望理论。研究结果表明,Vroom的动机期望理论可以用来解释年轻消费者使用AI工具进行购物决策的动机。动机可以是内在动机,外在动机或力量选择动机。期望值表示工具的易用性,工具性表示工具在执行所需任务中的能力,而价数表示对工具的满意度,奖励经验和对工具使用的信任。研究局限/含义本研究的结果基于扎根的理论方法,即归纳法。需要采用归纳法和演绎法的替代研究方法,以增强外部效度并概括结果。该研究仅限于印度年轻消费者的购物动机。尚未与其他消费者动机研究进行比较。因此,没有人声称Vroom的理论比其他动机理论更具优势。实际意义这项研究对发展中国家的零售商具有强烈的影响,发展中国家的零售商被视为新兴的零售市场,并且近年来引入了AI工具。 Vroom的期望理论将帮助零售商了解使用AI工具或购物的消费者动机。原创性/价值Vroom的期望理论以理解消费者在购物工具中使用AI工具的动机是基于扎根的理论方法生成的。

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