Search results

Lack of trust in construction projects will lead to poor project performance or project failure, indicating the importance of trust-building. Existing studies have developed various trust models, while most studies covered limited trust factors, failed to clarify their meanings and relationships or lacked qualitative or quantitative evidence. Thus, this study aims to develop a measurement model of trust in construction projects with theoretical justification as well as qualitative and quantitative data.

A literature review was conducted to identify conceptual types, factors and indicators of trust. Individual interviews and focus groups were performed to test the proposed framework with qualitative data. A survey and confirmatory factor analysis (CFA) method were utilized to build the measurement model of trust using quantitative data in BIM-assisted projects.

The proposed trust framework covered the four conceptual types, four factors (integrity, competency, benevolence and commitment) and 13 indicators, supported by the results of interviews and focus groups. The measurement model of trust from CFA results supported the significant, positive, and one-to-one relationships between 13 indicators and four factors of trust in BIM-assisted projects.

Theoretically, the study provides new insights into the multi-dimensional nature of trust. In practice, the findings could facilitate trustors and trustees to better understand, build, measure and enhance trust in construction projects.

Researchers have examined the influence of the factors on reducing return rates in retailing over the years. However, the returns experience is often an overlooked way to drive customer engagement and repeat sales in the now ubiquitous omnichannel setting. The focus on returns prevention in existing research overshadows management’s need to understand better the comprehensive mechanics linking the customer in-store return experience with their repurchase actions. Recognizing the need to bridge different stages of the returns management process, this research aims to explore the facilitators and barriers of in-store return activities.

Analysis of customer corporate data from 5,339 returns at the retail level provides insights from the customer return experience. Expanding our theoretical understanding, a deductive research approach then examines how those factors impact customer repurchase intentions both online and at brick-and-mortar stores. Stage two of the study employs a scenario-based role-playing experiment with consumer respondents to test hypotheses derived from signaling theory and justice theory.

Results find that returns policy and loyalty program capabilities are essential in creating a positive customer in-store experience. Moreover, a return experience enhanced by frontline employee service can retain existing shoppers and drive additional store traffic, further stimulating retailer sales.

These findings refine our understanding of returns management in evolving omnichannel retailing and offer practical insights for retailers to manage customer relationships through in-store returns.

When CEOs are publicly weighing in on sociopolitical debates, this is known as CEO activism. The steadily growing number of such statements made in recent years has been subject to a flourishing academic debate. This field offers first profound findings from observational studies. However, the discussion of CEO activism lacks a thorough theoretical grounding, such as a shared concept accounting for the heterogeneity of sociopolitical incidents. Thus, the aim of this paper is to provide an archetypal framework for CEO activism.

The authors used a multiple case study approach on 145 activism cases stated by CEOs and found seven distinct statement archetypes.

The study identifies four main structural design elements accounting for the heterogeneity of activism, i.e. the addressed meta-category of the statement, the targeted outcome, the used tonality and the orientation of the CEOs’ positions. Further, the authors found seven distinguishable archetypes of CEO activism statements: “Climate Alerts”, “Economy Visions”, “Political Comments”, “Self-reflections and Social Concerns”, “Tech Designs”, “Unclouded Evaluations” and “Descriptive Explanations”.

This typology classifies the heterogeneity of CEO activism. It will enable the analysis of interrelationships, mechanisms and motivations on a differentiated level and raise the comprehensibility of research-results.

The framework supports executives in understanding the heterogeneity of CEO activism and to analyse personality-fits.

To the authors’ knowledge, this marks the first conceptualisation of activism developed cross-thematically. The work supports further theory-building on CEO activism.

We aim to examine two issues. First, we intend to identify the best performing expected return proxies. Second, we investigate whether the expected return proxies for individual stocks can track the corresponding realized returns during extremely good or extremely bad times of the economic environment related to business conditions, stock market valuation and broad market performance.

We construct four sets of expected return proxies, including: (1) characteristic-based proxies; (2) standard risk-factor-based proxies; (3) risk-factor-based proxies that allow betas to vary with firm characteristics and (4) macroeconomic-variable-based proxies. First, we estimate expected returns for individual stocks using newly developed methods and evaluate the performance of these expected return proxies based on the minimum variance criterion of Lee et al. (2020). Second, we regress expected return proxies and realized returns on indicator variables that capture the extreme phases of the economic environment. Then we compare the estimated coefficients from these two sets of regressions and see if they are similar in magnitude via formal hypothesis testing.

We find that characteristic-based proxies and risk-factor-based proxies that allow betas to vary with firm characteristics are the two best performing proxies. Therefore, it is important to allow betas to vary with firm characteristics in constructing expected return proxies. We also find that model-based expected return proxies do a reasonably good job capturing actual returns during extremely bad and extremely good phases of business cycles measured by leading economic indicators, consumer confidence and business confidence. However, there is a large gap between the adjustment of model-based expected returns and realized returns during extreme episodes of stock market valuation or broad market performance.

We examine four types of expected return proxies and use the newly developed methodology as in Lee et al. (2020) to see which one is the best. In addition, we document whether model-based expected returns from individual stocks adjust partially or fully to keep pace with actual returns in response to changing economic conditions. No prior studies have examined these two issues.

This paper aims to extend the literature on social proof by looking at the effectiveness of social proof on behaviour change for environmental benefit.

The research is based on real case studies currently intended to encourage behaviour change among residents of a large UK city. An initial study assesses the motivation displayed within each case study. A second study then examines whether recipients recognise their own motivation in each case study.

Results indicate that participants did not recognise their own motivation in the case studies that were expected to be most similar to them, suggesting that recipients do not recognise “social proof” according to motivation. However, a relationship is observed between recipients’ gender and the gender of the case studies.

Demographics appear to be a better basis for social proof than motivation. This paper recommends several future avenues for further exploration, including using case studies that represent a wider range of characteristics (such as demographics). The current range of stimulus materials is limited, as these are real materials currently being used in a large UK city.

The results indicate that portraying motivation is not a good basis for using the social proof principle. Instead, social marketers ought to focus on representing similarity to the intended audience based on other characteristics such as gender.

The research contributes a new direction in this field, using Self-determination Theory to match social proof examples to recipients.

The purpose of this paper is to meet Dr Joyce Shaffer, PhD, ABPP, Clinical Associate Professor at the University of Washington.

This case study is presented in two sections: a positive autoethnography written by Joyce Shaffer, followed by her answers to ten questions.

In this positive autoethnography, Shaffer shares her life story and reveals numerous mental health and positive aging recommendations and insights for us to reflect on.

This is a personal narrative, albeit from someone who has been a clinical psychologist and active in the field of aging for many decades.

A pragmatic approach to aging is recommended. According to Shaffer, “those of us who can recognize the beat of the historical drummer can harvest the best of it and learn from the rest of it.”

Positive aging has strong social implications. Shaffer considers that it is not only about maximizing our own physical, mental, emotional and social health but also about maximizing that of others, to make our world a better place for everyone.

Positive aging can be experienced despite adversity. As Shaffer says, “Adversity used for growth and healed by love is the answer.”

Companies increasingly leverage artificial intelligence (AI) to enhance human performance, particularly in e-commerce. However, the effectiveness of AI augmentation remains controversial. This study investigates whether, how and why AI enhances human agents’ sales through a randomized field experiment.

This study conducts a two-by-two factorial randomized field experiment (N = 1,090) to investigate the effects of AI augmentation on sales. The experiment compares sales outcomes handled solely by human agents with those augmented by AI, while also examining the moderating effect of agents’ experience levels and the underlying mechanisms behind agents’ responses.

The results reveal that AI augmentation leads to a significant 5.46% increase in sales. Notably, the impact of AI augmentation varies based on agents’ experience levels, with inexperienced agents benefiting nearly six times more than their experienced counterparts. Mediation analysis shows that AI augmentation improves response timeliness, accuracy and sentiment, thereby boosting sales.

This study highlights the role of AI augmentation in human–AI collaboration, demonstrates the varying impacts of AI augmentation based on agents’ experience levels and offers insights for organizations on how to regulate AI augmentation to enhance agent responses and drive sales.