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Today, companies are struggling to develop their human resources analytics (HRA) capability, although interest in the subject is rapidly increasing. Furthermore, the academic literature on the subject is immature with limited practical guidance or comprehensive models that could support organisations in the development of their HRA capability. To address this issue, the aim of this paper is to provide a maturity model – i.e. HRAMM – and an interdependency matrix through which an organisation can (1) operationalise its HRA capability and assess its organisational maturity; (2) generate harmonious development roadmaps to improve its HRA capability; and (3) enable benchmarking and continuous improvement.

The research described in this paper is based on the popular methodology proposed by Becker et al. (2009) and the procedure for maturity evaluation developed by Gastaldi et al. (2018). This method combines academic rigour and field experience in analytics, in a process spanning eight main phases that involves literature reviews and knowledge creation techniques.

We define HRA maturity through four areas and 14 dimensions, providing a comprehensive model to operationalise HRA capability. Additionally, we argue that HRA maturity develops through an evolutionary path described in four discrete stages of maturity that go beyond traditional analytics sophistication. Lastly, the interdependency matrix reveals specific enablers for the development of HRA.

This paper provides practitioners with useful tools to monitor, evaluate and plan their HRA development path. Additionally, our research helps practitioners to prioritise their work and investment, generating an effective roadmap for developing and improving their HRA capability.

To the best of the authors’ knowledge, this study is the first to provide a model for evaluating the maturity of HRA capability plus an interdependency matrix to evaluate systematically the prerequisites and synergies among its constituting dimensions.

The purpose of this paper is threefold. First, it aims to clarify the moderating role of self-esteem (SE) and susceptibility to normative influence (SNI) in the relationship between brand love and brand loyalty. Second, the study proposes modeling the mediation role of brand love and outlining how SE and SNI affect the consumer-brand relationship. Finally, the study explores the impact of brand love on brand loyalty: the moderating role of self-esteem and social influences, as the literature regarding this is still lacking.

Data were collected via an online survey, which yielded 218 responses. Structural equation modeling was used to predict the research model.

The findings indicate that both SE and SNI mediate the relationship between brand love and brand loyalty. Additionally, consumers love the focal brands positively relates to SE and SNI. In return, SE and SNI lead to brand loyalty. The tight relationship of SE and SNI affects the connection between brand love and brand loyalty.

The data has been collected in Vietnam, which creates a limitation regarding the study’s cross-cultural nature and the economic context. Thus, the study should be conducted in different cultures and economies (both developing and developed countries) to enhance the generalizability in consumer-brand relationships.

Brand managers should conduct more advertising in brand communities to enhance the influence of SNI and emphasize unique features of the brands, to attract consumers through the overlap of SE.

The findings can contribute to enhancing unique brand identity and self-motivation will increase consumer loyalty, increasing the revenue of a specific brand. Moreover, as acceptable peers contribute to making purchase decisions, boosting the brand community will maintain current consumers and attract additional potential consumers from the current consumer relationships.

This study contributes to consumer psychology by indicating both SNI and SE as the mediators in the relationship between brand love and brand loyalty and how the consumer-brand relationship can be enabled.

El propósito de este trabajo es triple. En primer lugar, pretende aclarar el papel moderador de la autoestima (SE) y la susceptibilidad a la influencia normativa (SNI) en la relación entre el amor y la lealtad a la marca. En segundo lugar, el estudio propone modelar el papel mediador del amor a la marca y esbozar cómo la autoestima y la SNI afectan a la relación consumidor-marca. Por último, el estudio explora los factores que afectan a la relación entre el amor a la marca y la lealtad a la misma, ya que aún no existe literatura al respecto.

Los datos se recogieron mediante una encuesta en línea, que arrojó 218 respuestas. Se utilizó el modelo de ecuaciones estructurales (SEM) para predecir el modelo de investigación.

Los hallazgos indican que tanto la autoestima como la SNI median la relación entre el amor y la lealtad a la marca. Además, el amor de los consumidores por las marcas focales se relaciona positivamente con la autoestima y la SNI. En cambio, la autoestima y la SNI conducen a la lealtad a la marca. La estrecha relación de la autoestima y la SNI afecta la conexión entre el amor a la marca y la lealtad a la misma.

Los datos se han recogido en Vietnam, lo que crea una limitación en cuanto a la naturaleza transcultural del estudio y el contexto económico. Así pues, el estudio debería realizarse en diferentes culturas y economías (tanto de países en desarrollo como desarrollados) para aumentar la posibilidad de generalización en las relaciones entre consumidores y marcas.

Los gerentes de marca deberían hacer más publicidad en las comunidades de marcas para aumentar la influencia de la SNI y hacer hincapié en las características singulares de las marcas, a fin de atraer a los consumidores mediante la superposición de la autoestima.

Las conclusiones pueden contribuir a mejorar la identidad de una marca única, y la automotivación aumentará la lealtad de los consumidores, incrementando los ingresos de una marca específica. Además, como los pares aceptables contribuyen a la toma de decisiones de compra, el impulso de la comunidad de marcas mantendrá a los consumidores actuales y atraerá a otros consumidores potenciales de las relaciones de consumo actuales.

Este estudio contribuye a la psicología del consumidor al indicar que tanto la SNI como la autoestima son los mediadores en la relación entre el amor y la lealtad a la marca y la forma en que se puede habilitar la relación consumidor-marca.

摘要

本文有三方面的研究目的。第一，明确自尊（SE）和易受规范影响（SNI）在品牌喜爱与品牌忠诚关系中的调节作用。第二，建立品牌喜爱的中介作用模型，并概述SE和SNI如何影响消费者与品牌的关系。最后，探讨品牌喜爱对品牌忠诚度的影响：自尊和社会影响因素的调节作用，目前有关这方面的文献还比较缺乏。

通过在线调查收集数据，共收到218份答复。采用结构方程模型进行预测研究模型。

研究结果表明，自尊和易受规范影响都对品牌喜爱和品牌忠诚度之间的关系起到了调节作用。此外，消费者对焦点品牌的喜爱与自尊和易受规范影响具有正相关关系。反过来，自尊和易受规范影响又会导致品牌忠诚。自尊和易受规范影响的紧密关系影响了品牌喜爱和品牌忠诚之间的联系。

由于本次研究数据是在越南收集的，造成了研究的跨文化性质和经济背景的局限性。因此，未来的研究可以在不同的文化和经济体（包括发展中国家和发达国家）进行，以提高关于消费者与品牌关系结论的普遍性。

品牌管理者可以在品牌社区中增强广告宣传，提高易受规范影响的影响力，同时强调品牌的独特性，与自尊因素相结合来吸引消费者。

本文研究结果有助于增强独特的品牌识别和自我激励，提高消费者的忠诚度，增加特定品牌的收入。此外，由于合适的同伴有助于消费者做出购买决策，提升品牌社区可以维持现有消费者，并通过现有消费者关系吸引更多的潜在消费者。

本研究提出了自尊和易受规范影响是品牌喜爱和品牌忠诚关系的调节变量，并且进一步阐释了它们如何促成消费者与品牌的关系，从而为消费者心理学做出了贡献。

The amount of features in handwritten digit data is often very large due to the different aspects in personal handwriting, leading to high-dimensional data. Therefore, the employment of a feature selection algorithm becomes crucial for successful classification modeling, because the inclusion of irrelevant or redundant features can mislead the modeling algorithms, resulting in overfitting and decrease in efficiency.

The minimum redundancy and maximum relevance (mRMR) and the recursive feature elimination (RFE) are two frequently used feature selection algorithms. While mRMR is capable of identifying a subset of features that are highly relevant to the targeted classification variable, mRMR still carries the weakness of capturing redundant features along with the algorithm. On the other hand, RFE is flawed by the fact that those features selected by RFE are not ranked by importance, albeit RFE can effectively eliminate the less important features and exclude redundant features.

The hybrid method was exemplified in a binary classification between digits “4” and “9” and between digits “6” and “8” from a multiple features dataset. The result showed that the hybrid mRMR +  support vector machine recursive feature elimination (SVMRFE) is better than both the sole support vector machine (SVM) and mRMR.

In view of the respective strength and deficiency mRMR and RFE, this study combined both these methods and used an SVM as the underlying classifier anticipating the mRMR to make an excellent complement to the SVMRFE.