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In this paper, two studies examine the effects of Hispanic-targeted ads on three consumer-brand relationships (i.e. brand love, brand attachment and attitude toward the brand) and their behavioral outcomes. Additionally, this paper aims to examine how product involvement moderates these effects.

Study 1 demonstrates the linkages between three consumer–brand relationships (i.e. brand love, brand attachment and attitude toward the brand) and their antecedents. In Study 2, by testing the nomological network of the antecedents and consequences of brand love, the authors extend the results of the previous study.

Study 1 provides evidence that ethnic advertising has the strongest effect on brand love in comparison with other alternative consumer-brand relationship constructs. In Study 2, results show that only for low-involvement products, the perceived similarity of customers with Hispanic models in ads could lead to a higher positive attitude toward the ad. However, in the high-involvement condition, perceived similarity did not mediate the relationship between those constructs, which is in line with Elaboration Likelihood Model.

From the practical and managerial perspective, this paper proposes that managers should concentrate on ads with ethnic cues, especially for low involvement products. Hence, it would be worthwhile for companies to consider ads with ethnic cues to effectively develop deep consumer–brand relationships.

This paper investigates the effects of targeted ads on deeper relationships (e.g. brand love) and its behavioral consequences. Therefore, the authors show how Hispanic targeted ads could lead to deep consumer–brand relationships which encompass both the mind and heart of customers.

En este trabajo, dos estudios examinan los efectos de los anuncios dirigidos a los hispanos en tres relaciones entre el consumidor y la marca (el amor por la marca, el apego a la marca y la actitud hacia la marca) y sus resultados conductuales. Además, examinamos cómo la implicación del producto modera estos efectos.

El Estudio 1 demuestra los vínculos entre tres relaciones consumidor-marca (el amor a la marca, el apego a la marca y la actitud hacia la marca) y sus antecedentes. En el Estudio 2, al comprobar la red nomológica de los antecedentes y las consecuencias del amor a la marca, ampliamos los resultados del estudio anterior.

El Estudio 1 aporta pruebas de que la publicidad étnica es la que más influye en el amor a la marca, en comparación con otros constructos alternativos de relación entre los consumidores y la marca. En el Estudio 2, los resultados muestran que sólo en el caso de los productos de baja implicación, la similitud percibida de los clientes con los modelos hispanos en los anuncios podría conducir a una mayor actitud positiva hacia el anuncio. Sin embargo, en la condición de alta implicación, la similitud percibida no medió en la relación entre esos constructos, lo que está en consonancia con el Elaboration Likelihood Model.

Desde el punto de vista práctico y de gestión, este trabajo propone que los gestores se centren en los anuncios con señales étnicas, especialmente para los productos de baja implicación. Por lo tanto, valdría la pena que las empresas tuvieran en cuenta los anuncios con señales étnicas para desarrollar eficazmente relaciones más estrechas entre el consumidor y la marca.

Este trabajo investiga los efectos de los anuncios dirigidos en las relaciones profundas (i.e., el amor a la marca) y sus consecuencias conductuales. Por lo tanto, mostramos cómo los anuncios dirigidos a los hispanos podrían conducir a relaciones profundas entre el consumidor y la marca que abarcan tanto la mente como el corazón de los clientes.

本文的两项研究考察了以西班牙裔为目标的广告对三种消费者-品牌关系（即品牌喜爱、品牌依恋和对品牌的态度）及其行为结果的影响。此外, 我们还研究了产品参与是如何调节这些影响的。

研究1展示了三种消费者-品牌关系（即品牌喜爱、品牌依恋和对品牌的态度）与其前因之间的联系。另外, 通过在研究2中测试品牌热爱前因后果的法理网络, 我们扩展了之前研究的结果。

研究1提供的证据表明, 民族广告对品牌喜爱的影响强于其他的消费者品牌关系建构。研究2的结果显示, 与西班牙裔模特的相似性只有在低介入产品中被感知, 才会让顾客产生对广告的更高积极态度。然而, 在高介入条件下, 感知相似性并没有调解这些建构之间的关系, 这与细化可能性模型是一致的。

从实践和管理的角度来看, 本文建议管理者应该把注意力集中在有民族线索的广告上, 尤其是低介入产品。因此, 为了有效地发展深层次的消费者-品牌关系, 企业考虑带有民族线索的广告是值得的。

本文研究了目标广告对深层关系（如品牌喜爱）的影响及及其行为后果。因此, 我们展示了以西班牙裔为目标的广告是如何能够导致一个深层次, 顾客思想与心灵上的消费者-品牌深层关系。

Building on the routine dynamics literature, this paper aims to expand our philosophical, practical and infrastructural understanding of implementing lean production. The authors provide a process view on the interplay between lean operating routines and continuous improvement (CI) routines and the roles of different actors in initiating and establishing these routines.

Using data from interviews, observations and document analysis, retrospective comparative analyses of three embedded case studies on lean implementations provide a process understanding of enacting and patterning lean operating and CI routines in manufacturing SMEs.

Incorporating the “who” and “how” next to the “what” of practices and routines helps explain that rather than being implemented in isolation or even in conjunction with each other, sustainable lean practices and routines come about through team leader and employee enactment of the CI practices and routines. Neglecting these patterns aligned with unsustainable implementations.

The proposed process model provides a valuable way to integrate variance and process streams of literature to better understand lean production implementations.

The process model helps manufacturing managers, policy makers, consultants and educators to reconsider their approach to implementing lean production or teaching how to do so.

Nuancing the existing lean implementation literature, the proposed process model shows that CI routines do not stem from implementing lean operating routines. Rather, the model highlights the importance of active engagement of actors at multiple organizational levels and strong connections between and across levels to change routines and work practices for implementing lean production.

The purpose of this study is to empirically test how problem-solving lean practices, along with leaders as learning facilitators in an action learning approach, can be transferred from a production context to a knowledge work context for the purpose of becoming a learning organization while enhancing performance. This is important to study because many organizations struggle to enhance efficiency in the short term while still trying to be long-term learning oriented (i.e. learning organization development).

The authors draw on theory on learning interventions to show how lean practices for problem-solving can foster learning and help an organization to become adaptive. This study’s subject is a non-production department of 100 employees at the LEGO corporation. The authors applied survey results from a natural experiment lasting 18 months between a pre-measurement survey and a post-measurement survey. The results were compared to a control department of 50 employees who were not exposed to the lean practices intervention. The authors’ focus was on the individual level as individuals have different perceptions of lean practices, performance, and learning.

Using repeated-measures tests, difference-in-difference regressions analyses, and structural equation models, the authors find that a package of contemporary lean practices for problem-solving, along with leaders who function as learning facilitators, significantly improved learning organization dimensions while also enhancing efficiency and quality and that learning organizations positively mediate the relationship between the lean intervention and quality-related performance, while efficiency is directly affected by the lean interventions. Data from LEGO's key performance indicators (KPIs), benefit trackers, on-site observations and more than 40 interviews with managers provided results that were consistent with the survey data. A detailed description of the lean practices implemented is provided to inspire future implementations in non-operations environments and to assist educators.

The authors contribute to the learning literature by showing that a learning-to-learn approach to lean management can serve as an active and deliberate intervention in helping an organization becoming a learning organization as perceived by the individual organizational members. The authors also add to the lean literature by showing how a learning approach to lean, as used by LEGO, can positively affect short-term efficiency and quality and create a foundation for a longer-term competitive advantage (i.e. a learning organization) in a non-production context. By contrast, most of the lean literature streams treat efficiency separately from a learning organization and mainly examine lean in a production context.

The extant literature shows three research streams on lean, learning, and performance. The authors built on these streams by trying to emphasize both learning and efficiency. Prior research has not empirically tested whether and how the application of problem-solving lean practices combined with leaders as learning facilitators helps to create a comprehensive learning organization while enhancing performance in a non-production context.

This paper identifies the challenges during a recent disaster relief operation in a developing country where the humanitarian response is dominated by national actors, with international actors having a minor role.

A case study design is used; the main data sources are semi-structured interviews with 43 informants involved in the 2017 Kermanshah earthquake relief operation.

The findings suggest that humanitarian practitioners deal with multiple challenges during disaster relief operations. One group of challenges relates to humanitarian logistics (HL) like needs assessment, procurement, warehousing, transportation and distribution, all widely discussed in the literature. Another involves the growing use of social media, legitimacy regulations and the engagement of new humanitarian actors (HAs) like social media activists and celebrities. These factors have not been extensively studied in the literature; given their growing influence, they require more scholarly attention.

The findings will help humanitarian practitioners and policymakers better understand the challenges involved in disaster relief operations conducted by multiple actors and thus help them improve their practices, including the creation of proper regulations, policies and logistics strategies.

The study uses primary data on a recent disaster to assess and extend the findings of previous studies regarding HL challenges. It also elaborates on the critical non-logistical challenges that influence aid delivery in emergency responses, including the growth of social media, regulations and the engagement of new HAs. The results may motivate future empirical and modelling studies to investigate the identified challenges and identify practices to mitigate them.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The role of retailers in influencing consumer attitude during a brand scandal is quite complex, as retailers are in direct contact with both marketers and consumers. The purpose of the exploratory research is to propose a theoretical model to capture the influences retailers exercise on consumers during brand scandals.

A qualitative approach has been adopted in the study. The study employs the grounded theory approach on the data collected by conducting in-depth interviews with 25 retailers.

Four contextual conditions and six behavioral antecedents of the retailer's role in the context of the brand scandal were identified. Then, the study finds that companies tend to follow two broad approaches during a brand scandal to address retailers' queries and apprehensions. On these bases, the study proposes a six-pronged typology to better understand retailers' role in shaping consumers' brand perception.

Existing literature has not paid adequate attention to this aspect of retailers' role in influencing consumer choices during brand scandal. To the best of the authors' knowledge, there is no prior research which investigates the role and influence of retailers in shaping consumer attitude during brand scandals. It is important to underline that the current research advocates retailers' significant role during a performance-based brand scandal. Specifically, the authors explored a health-related defective scandal of a well-known food brand. In addition, the study focuses on traditional grocery retailers, which already have special relationships with their consumers. Based on retailer perspectives, the authors' contribution is also updating the discussion of branding theory in case of scandals. The identified variables and constructs may be used for empirical investigation on the role of retailers in shaping consumer attitudes toward the scandalized brand.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

As a problem-solving tool, the kaizen event (KE) is underutilised in practice. Assuming this is due to a lack of group process quality during those events, the authors aimed to grasp what is needed during high-quality KE meetings. Guided by the phased approach for structured problem-solving, the authors built and explored a measure for enriching future KE research.

Six phases were used to code all verbal contributions (N = 5,442) in 21 diverse, videotaped KE meetings. Resembling state space grids, the authors visualised the course of each meeting with line graphs which were shown to ten individual kaizen experts as well as to the filmed kaizen groups.

From their reactions to the graphs the authors extracted high-quality KE process characteristics. At the end of each phase, that should be enacted sequentially, explicit group consensus appeared to be crucial. Some of the groups spent too little time on a group-shared understanding of the problem and its root causes. Surprisingly, the mixed-methods data suggested that small and infrequent deviations (“jumps”) to another phase might be necessary for a high-quality process. According to the newly developed quantitative process measure, when groups often jump from one phase to a distant, previous or next phase, this relates to low KE process quality.

A refined conceptual model and research agenda are offered for generating better solutions during KEs, and the authors urge examinations of the effects of well-crafted KE training.

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.

Why are some lean workfloor teams able to improve their already high performance, over time, and others not? By studying teams' and leaders' behaviour-value patterns, this abductive field study uncovers a dynamic capability at the team level.

Various methods were employed over three consecutive years to thoroughly examine five initially high-performing lean workfloor teams, including their leaders. These methods encompassed micro-behavioural coding of 59 h of film footage, surveys, individual and group interviews, participant observation and archival data, involving objective and perceptual team-performance indicators. Two of the five teams continued to improve and perform highly.

Continuously improving high lean team performance is found to be associated with (1) team behaviours such as frequent performance monitoring, information sharing, peer support and process improvement; (2) team leaders who balance, over time, task- and relations-oriented behaviours; (3) higher-level leaders who keep offering the team face-to-face support, strategic clarity and tangible resources; (4) these three actors' endorsement of self-transcendence and openness-to-change work values and alignment, over time, with their behaviours; and (5) coactive vicarious learning-by-doing as a “stable collective activity pattern” among team, team leader, and higher-level leadership.

Since lean has been undertheorised, the authors invoked insights from organisational behaviour and management theories, in combination with various fine- and coarse-grained data, over time. The authors uncovered actors' behaviour-value patterns and a collective learning-by-doing pattern that may explain continuous lean team performance improvement. Four theory-enriching propositions were developed and visualised in a refined model which may already benefit lean practitioners.