Author Archives: support

Study Reveals Group Sales Incentives Improve Underperforming Brand Sales

New research co-authored by a UC Riverside business professor offers valuable insights for managers of retail outlets that focus on a single brand: it is crucial to consider the brand’s strength when designing sales staff pay incentives. The study examines “brand-managed” retail operations, which include “stores within stores,” such as cosmetics counters in major department stores staffed by salespeople dedicated to a single brand like Clinique. It also covers standalone stores in shopping malls that feature products from one brand, such as Nike sportswear or Gap clothing.

Subramanian Balachander, a marketing professor at UC Riverside, and his collaborators discovered that group incentives, like sales commissions shared equally among sales team members, lead to better sales performance for weaker brands in these specific retail settings. In contrast, individual incentives based on each salesperson’s sales volumes proved more effective for more substantial brands. While these findings may appear counterintuitive, Balachander clarified that weaker brands typically experience more unpredictable sales outcomes. If salespeople are individually compensated, the store risks overpaying some salespeople for easier sales to repeat buyers or customers inclined to purchase upon arrival.

“With a weaker brand, group sales compensation serves as a better filter because it eliminates the concern of whether a particular salesperson made an easy sale or dealt with a difficult customer. It ensures that all salespeople are collectively working towards converting new customers, which is significantly more beneficial for a weak brand,” explained Balachander, who holds the Albert O. Steffey Chair at UCR’s School of Business. The study’s findings are based on data from brand-managed retail operations in the United States and China.

In the U.S., researchers collected data on the prevalence of group compensation in brand-managed outlets by examining designer brand names for beauty and fashion lines offered by a high-end department store and standalone stores at the Mall of America in Bloomington, Minnesota. Employee-reported information about salesperson incentives, such as commissions or cash bonuses offered by brands, was gathered from Glassdoor.com and Indeed.com. In China, researchers obtained monthly sales data from 23 gold jewellery brands sold in a large retail store, each with its own counters and sales staff. Similarly, sales data was collected from a major Chinese electronics retailer, earning a percentage of the sales revenue from 51 brands in the store, each with its own selling counter and salespeople.

The study also presents a model illustrating how brand strength or equity, influenced by marketing, promotion, customer awareness, and other factors affecting customer perception before visiting the store, impacts the selling effectiveness of salespersons. By understanding these dynamics, managers can better structure their sales staff incentives to align with the strength of their brand, ultimately improving sales performance and operational efficiency in brand-managed retail environments.

More information: Wenshu Zhang et al, Group or Individual Sales Incentives? What Is Best for Brand-Managed Retail Sales Operations?, Journal of Marketing. DOI: 10.1177/00222429241249424

Journal information: Journal of Marketing Provided by University of California, Riverside

The Dual Impact of Advertising on Life Satisfaction: A Comprehensive Global Study

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Advertising has long been recognised for creating a virtual reality of life, leading some individuals to develop expectations surpassing practical expectations. This phenomenon is driven by idyllic imagery, storytelling, and an emphasis on materialism, among other techniques.

Consequently, critics argue that advertising fosters unrealistic consumer expectations, which can lead to disappointment regarding overall life satisfaction. On the other hand, some posit that advertising enhances life satisfaction by providing valuable information that consumers can use to improve their lives.

These contrasting perspectives were the impetus for a comprehensive and thorough research, representing a rare and comprehensive examination of the relationship between consumer life satisfaction and advertising. The study, titled “A Longitudinal Examination of the Relationship Between National-Level Per Capita Advertising Expenditure and National-Level Life Satisfaction Across 76 Countries,” was published in the peer-reviewed INFORMS journal Marketing Science. The authors include Michael Wiles from Arizona State University, Saeed Janani from the University of Denver, Darima Fotheringham from Texas Tech University, and Chadwick Miller from Washington State University.

“Given advertising’s ubiquity and its influence on consumption decisions, it is reasonable to consider that advertising may also be linked to life satisfaction,” says Wiles. “Advertising can serve as an effective mechanism for disseminating information, which individuals rely on to gain knowledge about consumption options, ultimately enhancing life satisfaction.”

Wiles adds, “Another perspective on advertising suggests that it distorts individuals’ preferences towards consumption options that they may neither need nor want. Consequently, advertising can establish unrealistic or unhealthy consumption norms, potentially diminishing life satisfaction.”

The researchers delved into these issues by analysing patterns in a global context, covering 76 countries from 2006 to 2019. They examined the association between per capita advertising expenditure and national-level life satisfaction. To achieve this, they developed empirical research models to analyse perception data, utilised secondary data, and conducted a series of experiments.

“We discovered that per capita advertising expenditure is positively correlated with national average life satisfaction,” says Wiles. “However, we also found that excessive advertising can have negative effects, particularly when considering factors such as culture, income, and inequality. In certain contexts, advertising can contribute to reduced life satisfaction.”

“The takeaway from our research is that when advertising is utilised to reduce marketplace uncertainty, it can be a powerful force for good,” Wiles concludes. “Nonetheless, it is crucial to remain aware that this force can sometimes overemphasise materialism and, as a tool for life satisfaction, its impact can vary across different settings.”

More information: Michael A. Wiles et al, A Longitudinal Examination of the Relationship Between National-Level Per Capita Advertising Expenditure and National-Level Life Satisfaction Across 76 Countries, Marketing Science. DOI: 10.1287/mksc.2021.0136

Journal information: Marketing Science Provided by Institute for Operations Research and the Management Sciences

Artistic Value and Market Trends: A Comprehensive Bibliometric Analysis

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The intersection of culture and commerce within the art market has consistently captivated economists and art enthusiasts alike. A recent article presents an in-depth bibliometric analysis that spans five decades, scrutinising art pricing mechanisms and market efficiency metrics. It also sheds light on the pivotal role played by the COVID-19 pandemic in hastening digital transformations within the market. This analysis is instrumental in furnishing future research with a solid framework for dissecting the intricate relationship between art, economics, and culture.

The art market has been evolving for centuries, with the most notable changes occurring in recent years. Researchers have explored various dimensions, including art pricing, artist branding, the impact of digital platforms, and regulatory frameworks. Despite the breadth of research conducted, there remain significant gaps in understanding the full extent of market efficiency and the dynamic shifts brought about by technological advancements and global events. These gaps underscore the urgent need for comprehensive studies that delve into these areas, particularly considering the recent changes in market behaviours and regulatory environments. The urgency of these studies is paramount, as they can bring significant benefits to our understanding of the art market.

In a thorough review published in Data Science and Management on April 13, 2024, researchers from the Academy of Mathematics and Systems Science at the Chinese Academy of Sciences have conducted a comprehensive bibliometric analysis covering the last fifty years of the art market. The study employs big data and various analytical techniques to outline vital trends, influential works, and emerging themes within the sector.

The researchers analysed a substantial dataset from the Web of Science Core Collection, which included 912 pieces of literature on the art market from 1972 to 2021. The study delineated prominent trends and shifts in research focus by utilising descriptive statistical analysis and various bibliometric methods, such as co-citation and co-word analysis. Notably, there was a steady annual increase in research interest in the art market, with significant pivots from traditional topics like hedonic art prices to more contemporary issues such as artist brand management, digital art platforms, and anti-money laundering measures. The pandemic’s role in accelerating digital market transformations was particularly emphasised. The study identified five primary research clusters, including anonymous painting and artistic brands, hedonic art price indices, digital art platforms, anti-money laundering regulation, and market efficiency, each representing key focal points within the field.

Dr Yunjie Wei, a leading author from the Academy of Mathematics and Systems Science, commented, “Our findings highlight the shifting priorities within art market research, especially the increasing relevance of digital platforms and regulatory frameworks. This exhaustive analysis charts historical trends and pinpoints crucial areas for future investigation, providing invaluable insights for scholars and practitioners.”

These findings have profound implications for academic research and practical application within the art market. The study pinpoints key trends and emerging focus areas, offering critical insights for stakeholders, including art dealers, auction houses, and regulatory bodies. These insights are crucial for navigating the complexities associated with digital transformation and regulatory compliance. The research underscores the need for continuous monitoring and analysis to stay abreast of the rapidly evolving art market landscape, ensuring stakeholders are well-informed and responsive to new trends and regulations.

More information: Mingjun Guo et al, analysis of the art market: from art price to market efficiency, Data Science and Management. DOI: 10.1016/j.dsm.2024.03.006

Journal information: Data Science and Management Provided by KeAi Communications Co. Ltd.

Could Large Language Models Potentially Serve as Replacements for Human Participants in Market Research Endeavours of the Future?

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According to a recent study published in the INFORMS journal Marketing Science, a fascinating possibility emerges for market researchers. The study, titled “Determining the Validity of Large Language Models for Automated Perceptual Analysis,” suggests that large language models (LLMs) could potentially replace human participants in research without a significant loss in data quality. This groundbreaking research, led by Peiyao Li and Zsolt Katona from the University of California, Berkeley, Noah Castelo from the University of Alberta, and Miklos Sarvary from Columbia University, opens up a new frontier in market research.

The researchers discovered that datasets generated by humans and LLMs exhibited agreement rates ranging from 75% to 85%. This finding underscores the potential of LLMs to accurately mimic human responses, thus offering a promising alternative for market research, particularly in perceptual analysis. The authors used LLMs to access and analyse data widely available online, creating a novel methodology that allows market researchers to rely solely on machine-generated data.

Peiyao Li explains that LLMs can generate text from specific prompts provided on various generative AI platforms. The study focuses on the automated analysis of market perceptions by developing this new workflow. By doing so, it demonstrates that LLM-powered market research can yield meaningful insights and effectively replicate results traditionally obtained from human surveys.

Zsolt Katona notes that although human interviews are optional using LLMs, the initial data originates from human inputs. This aspect highlights the models’ ability to learn from human perceptions, attitudes, and preferences to generate comparable responses.

Noah Castelo elaborates on the process, mentioning that the LLM uses prompts to produce text continuations, which can then assess and compare different brands or products within specific categories. The current agreement rates with human-generated research stand between 75% and 85%, proving the efficacy of LLMs in these applications.

Their method could revolutionise market research by enhancing efficiency, reducing timescales, and lowering costs for specific product and brand categories. However, they caution that this approach might yield inaccurate results across all categories and stress the importance of human oversight in particular contexts.

Miklos Sarvary expresses optimism about the future of LLM-based market research, anticipating that it will be capable of addressing more complex and nuanced questions as the technology and methodologies evolve. This research marks just the beginning of what could become a more widespread application of AI in market research. Importantly, it underscores the continued need for human oversight and expertise, pointing towards a future where LLMs and human researchers work in tandem to gather and analyse market data.

More information: Peiyao Li et al, Frontiers: Determining the Validity of Large Language Models for Automated Perceptual Analysis, Marketing Science. DOI: 10.1287/mksc.2023.0454

Journal information: Marketing Science Provided by Institute for Operations Research and the Management Sciences

New study reveals adverse consumer reactions to BLM support by major companies and brands

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New research in the INFORMS journal Marketing Science has discovered that companies and brands aligning themselves with Black Lives Matter (BLM) faced a negative impact from consumers. The study, titled “How Support for Black Lives Matter Impacts Consumer Responses on Social Media,” found that BLM support led to a decline in consumer engagement on social media, evidenced by fewer followers and “likes,” as well as an uptick in negative comments on social media posts.

Authored by Yang Wang, Marco Shaojun Qin, Xueming Luo, and Yu (Eric) Kou from Temple University’s Fox School of Business, the study aimed to explore how consumers react to brands taking a stand on social media about racial justice movements. The researchers examined the relationship between a brand’s BLM support and social media follower growth by analysing 503 BLM posts from 430 brands between June 1, 2019, and October 31, 2020. To determine causal impact, the study also focused on “Blackout Tuesday,” a major BLM support event on Instagram but not Twitter, which served as the “control platform.” Using data from 435 major brands in various industries and their 396,988 social media posts on both platforms, the researchers consistently found that BLM support triggered an adverse consumer reaction.

The researchers employed natural language processing deep learning tools to study the effects across brands by examining historical posts and concurrent unrelated, self-promotional posts. This analysis revealed that the adverse impact of BLM support was significantly amplified when brands posted content unrelated to BLM and self-promotional. These “off-topic” promotional posts exacerbated the negative effects of the brands’ BLM support.

To address the potential political or ideological motivations behind adverse consumer reactions, the researchers examined how customers’ political affiliations influenced their responses to brands’ BLM support. This analysis showed that mostly Republican consumers who did not support the BLM movement constituted a significant portion of the negative responses. However, another considerable segment of adverse reactions came from Democrat consumers who viewed brands as engaging in “slacktivism,” where a brand expresses support for a cause without backing it up with financial donations.

“One interesting finding is that negative associations were stronger when more brands posted in support of BLM while also posting self-promotional messages,” says Wang. “This suggests that large-scale BLM allyship programs combined with self-promotional posts created a ‘bandwagon effect’ that negatively impacted those brands.” “Brands that sought to capitalise by aligning with prominent racial justice movements should have exercised caution,” adds Luo. “They should not have been too quick to resume business as usual with their product promotions while supporting BLM. Nevertheless, brands with a history of prosocial posting on social media and socially oriented missions suffered less from the negative effects and might even benefit from supporting BLM.”

The authors suggest that brands that do not want to remain silent on major racial justice issues should consider making “prosociality” a core part of their social media strategy and brand mission long before such issues trend in the news and on social media. Brands should not appear as though their support is an afterthought or driven by “bandwagon” motives.

More information: Yang Wang et al, Frontiers: How Support for Black Lives Matter Impacts Consumer Responses on Social Media, Marketing Science. DOI: 10.1287/mksc.2022.1372

Journal information: Marketing Science Provided by Institute for Operations Research and the Management Sciences

Research indicates AI-driven cyberattacks could harm GDP and disrupt supply chains in major global economies

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Artificial Intelligence (AI) driven cyberattacks pose unprecedented risks to global economies, supply chains, and international trade, as detailed in a forthcoming study from Risk Analysis. Unlike traditional cyber threats relying on manual or scripted methods, AI-driven attacks leverage machine learning algorithms to enhance their effectiveness, stealth, and adaptability. These attacks autonomously learn and evolve strategies based on real-time feedback and environmental changes, presenting a dynamic challenge for cybersecurity.

The study uses simulation scenarios to explore the potential impacts of AI-driven cyberattacks on economies heavily reliant on digital technologies and interconnected supply chains. It reveals significant economic repercussions, including natural GDP declines, trade route disruptions, and fluctuations in trade prices and volumes across various regions. Major economies like China, the U.S., the U.K., and the E.U., which are deeply integrated into global networks, are particularly vulnerable.

Economically, the study depicts varying degrees of GDP reduction under different cyber threat scenarios. Even a low-level cyber threat with limited breaches could lead to minor decreases in real GDP, ranging from 0.02% to 0.25%. Countries such as China, Japan, and South Korea, with substantial involvement in world trade and intra-industry solid connections, may experience slightly higher declines. In contrast, a high-level cyber threat scenario results in more pronounced economic disruptions, potentially causing significant GDP reductions in major economies like the U.S., the U.K., the E.U., China, Japan, and India.

The impact extends beyond GDP to trade prices, where all regions experience deteriorating terms of trade following an AI cyberattack. Export prices rise less than import prices, particularly affecting economies heavily reliant on digital technologies and interconnected supply chains. The U.S. may see sharper declines in terms of trade due to its high dependency on exports and digital infrastructure. Moreover, trade routes are disrupted in high-level cyberattack scenarios, prompting major trading partners like China and the U.S. to seek alternative routes, benefiting intermediary countries such as India, Japan, and North and Latin American nations.

The findings underscore the urgent need for global efforts to enhance cyber resilience and mitigate the far-reaching impacts of AI-driven cyber threats on the interconnected global trade ecosystem. The study strongly advocates for proactive measures such as adaptable production systems, diversified trade partnerships, and robust cybersecurity infrastructure to mitigate the adverse effects of cyberattacks. Dr Sherif Elgendy, a researcher involved in the study, emphasizes the critical role of preparedness in combating digital warfare, suggesting that incorporating cyber resilience measures can significantly mitigate the negative consequences highlighted in the study.

This research illuminates AI-driven cyberattacks’ multifaceted and pervasive nature on global economies, supply chains, and trade networks. It highlights the need for concerted global efforts to bolster cyber resilience and safeguard the interconnected systems that underpin modern economies. By taking proactive steps to enhance cybersecurity preparedness and international cooperation, stakeholders can mitigate the potentially catastrophic impacts of AI-driven cyber threats and navigate the evolving landscape of digital warfare more effectively.

More information: Rehab Osman et al, Interconnected and resilient: A CGE analysis of AI-driven cyberattacks in global trade, Risk Analysis. DOI: 10.1111/risa.14321

Journal information: Risk Analysis Provided by Society for Risk Analysis

Sunshine Boosts Spending: Investors Wager on Sunny Days

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Research at the University of South Australia reveals a compelling link between weather conditions and investment behaviour, particularly concerning lottery-like stocks. These stocks, akin to lottery tickets in their potential for high returns amid high risk, witness heightened investor interest during sunny, clear-sky days. Dr Reza Bradrania, Senior Lecturer of Finance at UniSA’s Centre for Markets, Values and Inclusion, underscores the psychological impact of weather on human mood, citing studies indicating that sunshine can significantly uplift spirits.

The study, a pioneering endeavour in its field, draws on an extensive 36-year weather data set from US weather stations, encompassing metrics like cloud cover, wind speed, rainfall, and temperature. Simultaneously, it analyses stock price data from the same period to discern patterns in investor behaviour. Dr. Bradrania and PhD student Ya Gao discovered a marked tendency among investors to embrace greater risk and optimism on pleasant weather days, leading to increased investment in lottery-like stocks.

The phenomenon, rooted in the psychological concept that good moods spur risk-taking, suggests that sunny weather fosters optimism about the potential returns of such speculative investments. This optimism, however, can sometimes translate into overconfidence and subsequent adjustments in stock prices, often resulting in significant losses. Dr. Bradrania notes that while weather’s influence on mood is well-documented, its impact on financial decision-making, particularly in high-risk investments, remains a relatively unexplored frontier.

The implications of this research extend beyond mere curiosity, offering insights into how external environmental factors like weather can sway investor judgements and market dynamics. By highlighting the correlation between weather patterns and gambling-like investment preferences, the study underscores the nuanced interplay between psychological states, weather conditions, and financial decision-making.

Furthermore, the findings suggest practical applications for investors and financial analysts alike. Recognising the potential for weather to influence investor sentiment and risk appetite, stakeholders can factor in weather forecasts as a supplementary tool in predicting market trends. This approach could help mitigate the effects of overconfidence and speculative bubbles that may arise during periods of prolonged good weather.

The University of South Australia’s research underscores the intricate relationship between weather conditions and investment behaviours, shedding light on how environmental factors can subtly shape financial markets. By revealing the impact of sunny days on investor optimism and risk-taking in lottery-like stocks, the study paves the way for deeper insights into the psychology of financial decision-making. It offers practical implications for navigating market fluctuations under varying weather conditions.

More information: Reza Bradrania et al, Lottery demand, weather and the cross-section of stock returns, Journal of Behavioral and Experimental Finance. DOI: 10.1016/j.jbef.2024.100910

Journal information: Journal of Behavioral and Experimental Finance Provided by University of South Australia

Harnessing AI-Driven Tactics for Maximizing Cryptocurrency Gains Amid Market Volatility

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In the ever-evolving realm of cryptocurrencies, managing volatility remains a critical challenge. A pioneering study recently unveiled a novel approach integrating Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) with genetic algorithms and neural networks to enhance trading precision amidst market fluctuations.

Since its inception in 2009, Bitcoin has sparked a surge in the popularity of cryptocurrencies, a trend marked by rapid growth and significant volatility. This volatile landscape has spurred the need for advanced analytical tools to navigate its unpredictable nature. The researchers, in their quest, evaluated various machine learning models, including Adaptive Genetic Algorithms with Fuzzy Logic and Quantum Neural Networks. Their key finding was the significant performance boost these models experienced when integrated with EGARCH, effectively enhancing prediction accuracy by modelling cryptocurrency price volatility. The cryptocurrency X2Y2, in particular, demonstrated the highest prediction accuracy, underscoring the potential of merging advanced machine learning methods with volatility models to mitigate trading risks and refine investment strategies.

Lead researcher Dr. David Alaminos from the University of Barcelona remarked, “Our approach leverages neural networks and genetic algorithms, enhanced by EGARCH’s volatility modelling capabilities. This synergy enables more reliable predictions of market movements and significantly reduces trading risks.”

This innovative methodology equips investors with essential tools to minimize risks in cryptocurrency investments. The insights gleaned from this research could also prove invaluable to regulatory bodies in formulating policies that promote market fairness and stability. Furthermore, developers can leverage these findings to advance predictive algorithms for financial technologies, thereby enhancing the industry’s analytical capabilities and risk management strategies.

More information: David Alaminos et al, Managing extreme cryptocurrency volatility in algorithmic trading: EGARCH via genetic algorithms and neural networks, Quantitative Finance and Economics. DOI: 10.3934/QFE.2024007

Journal information: Quantitative Finance and Economics Provided by Maximum Academic Press

Impact of Comparison Options on Stock Purchase Decisions

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When a company first goes public on the stock exchange, the corresponding securities are known as IPO (initial public offering) shares. These shares typically exhibit below-average returns for the initial years following the offering, except for a few outliers that surge in value right from the start. In other words, the likelihood of achieving high returns is relatively low. Why, then, do people still purchase IPO shares? The reason is their tendency to overestimate the probability of the stock becoming one of the rare super performers. This phenomenon is explained by prospect theory, a leading theory that elucidates decision-making under uncertainty. This is akin to why people buy lottery tickets: they hope to hit the jackpot.

Some investments yield a very different distribution of profits and losses, usually characterised by a high likelihood of small returns. This is the standard scenario. Conversely, significant losses are improbable, such as with catastrophe bonds or “cat bonds”. Insurance companies use these bonds to create a financial cushion to guarantee coverage in a disaster. If nothing happens, investors receive a series of small payouts. However, all the invested money is lost in the statistically unlikely event of a natural disaster.

What circumstances influence how people select a particular type of security in the first place? Dr. Sebastian Olschewski from the Faculty of Psychology has published a study on this topic in the journal PNAS. In the experiment, participants were asked to choose between two or three different stocks, for example, one offering “a low probability of high returns” and another offering “a high probability of modest returns with rare but potentially high losses”. To aid in the decision-making process, information was provided about the performance of the stocks, i.e., when and what returns were generated by the specific stocks on day 1, day 2, day 3, etc. This allowed participants to closely examine the volume and frequency of each stock’s returns.

The results showed that the ability to compare different stock types significantly influences a person’s decision, favouring investments on the cat bond end of the spectrum. “In our experiment, the participants selected stocks that generated the highest returns on the greatest number of days. The overall total of the returns had only an ancillary effect.” This is what experts refer to as the “frequent winner effect”. To demonstrate the significance of this effect, the data on stock returns was modified in a second experimental design so that the “lottery-like” investments more frequently showed higher yields, quickly shifting participants’ preference towards this type of stock.

What conclusions can be drawn from the study? “If we want to predict how the stock market will perform, we also need to consider how people go about finding information,” says Olschewski. “Whether they simply research a single stock or compare two or three options.” Predicting such behaviour is crucial for economists or analysts who aim to forecast price trends in the stock market. It is also essential for social resource planning, such as governments investing to benefit their citizens. For example, the Swiss pension system is partially funded in the capital market, as Olschewski points out.

More information: Sebastian Olschewski et al, Frequent winners explain apparent skewness preferences in experience-based decisions, Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.2317751121

Journal information: Proceedings of the National Academy of Sciences Provided by University of Basel

AI Revolutionises Customer Service: Boosting Flexibility and Efficiency

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Whenever you contact a customer service centre, the team on the other end typically aims to achieve three goals: reduce response time, solve your issue, and do so within the shortest possible service duration. However, resolving your issue might require a considerable time investment, potentially conflicting with the overarching business objective of minimising service duration. These conflicting priorities are common in customer service centres, which rely on the latest technology to meet customer needs.

To manage these conflicting demands, organisations practise what is known as ambidexterity, which can be achieved through three modes: structural separation, behavioural integration, and sequential alternation. So, how might artificial intelligence (AI) systems enhance these organisations’ ability to transition between ambidexterity modes to accomplish their tasks? New research involving the School of Management at Binghamton University, State University of New York, delved into this question. Using data from various contact centre sites, researchers examined the impact of AI systems on a customer service organisation’s ability to shift across ambidexterity modes.

The key finding: it’s a delicate balancing act. When used correctly, AI is a valuable asset, but organisations should not rely exclusively on it to guide their strategies. Associate Professor Sumantra Sarkar, who co-conducted the research, stated that the study aimed to understand better how organisations might use AI to guide their transition between ambidexterity modes, as different structures or approaches might be more beneficial at other times. “Customer service organisations often balance exploiting the latest technology to boost efficiency and save money,” Sarkar said. “This dichotomy is what ambidexterity is all about—exploring new technology to gain new insights and exploiting it to gain efficiency.”

As part of the three-year study, researchers examined the practices of five contact centre sites: two global banks, one national bank in a developing country, a Fortune 500 telecommunications company in South Asia, and a global infrastructure vendor in telecommunications hardware. While many customer service organisations have invested in AI in recent years, assuming that failing to do so could lead to customer dissatisfaction, the researchers found that these organisations have yet to use AI to its full potential. They have primarily utilised it for self-service applications.

Some AI-assisted tasks tracked by researchers at these sites included using AI systems to automatically open applications, send emails, transfer information between systems, approve or disapprove loan applications, and provide personalised service based on customer data and contact history. Researchers determined that while it is beneficial for customer service companies to harness AI’s advantages and navigate its challenges, they should not do so at the expense of supporting quality professional development and ongoing learning opportunities for their staff.

Sarkar emphasised that to utilise AI’s benefits fully, leaders of customer service organisations need to examine every customer touchpoint and identify opportunities to enhance the customer experience while improving operational efficiency. Consequently, Sarkar advised newcomers in the technology-savvy industry to learn from companies with 20 or 30 years of experience, especially in adapting to technological changes, including AI, before formulating their business strategies. “Any business is a balancing act because decisions made at the beginning of the year based on forecasts must be continually revised,” Sarkar said. Given the added tension within customer service organisations about whether to focus on efficiency or exploration, they must work even harder to strike that balance. Effectively using AI helps them achieve this.”

More information: Lan Cao et al, Shift of ambidexterity modes: An empirical investigation of the impact of artificial intelligence in customer service, International Journal of Information Management. DOI: 10.1016/j.ijinfomgt.2024.102773

Journal information: International Journal of Information Management Provided by Binghamton University

Effectiveness of Mandatory Retirement Plans in Promoting Retirement Savings

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A study published in Contemporary Economic Policy highlights the substantial benefits observed following the initial implementation of Oregon’s state-run retirement savings program, OregonSaves, in 2017.

OregonSaves targets Oregon workers employed by entities that do not offer a workplace retirement plan, self-employed individuals, and others in similar situations. Employers who do not provide retirement plans are mandated to automatically enrol their employees, although workers can opt-out at any point.

The study’s analysis reveals a significant increase in retirement savings among previously uncovered private sector employees, a clear testament to the program’s effectiveness. Furthermore, Oregon workers experienced a noticeable 12% rise in Individual Retirement Account (IRA) ownership after the program’s introduction.

Of particular significance are the substantial gains observed among lower-income, single, and older workers, as well as employees of very small-scale businesses. This underscores the program’s inclusive nature and its ability to benefit a wide range of individuals who previously lacked access to retirement savings options.

According to Ngoc Dao, PhD, corresponding author from Kean University, “State-mandated retirement savings policies represent an effective strategy for narrowing savings disparities, particularly among low-income workers. The Oregon model demonstrates significant public policy benefits in enhancing retirement savings for individuals without access to employer-sponsored retirement plans.”

More information: Ngoc Dao et al, Does a requirement to offer retirement plans help low-income workers save for retirement? Early evidence from the OregonSaves program, Contemporary Economic Policy. DOI: 10.1111/coep.12648

Journal information: Contemporary Economic Policy Provided by Wiley

Changing market dynamics contribute to consumer distraction, increasing the likelihood of risky purchases

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Researchers have formulated a new theory regarding how shifting market dynamics can prompt significant numbers of typically cautious consumers to opt for risky purchases, such as subprime mortgages, cryptocurrencies, or even cosmetic surgery procedures.

These changes often begin in product categories initially perceived as low-risk upon market entry. As demand grows, more companies may enter the market, offering cheaper product versions with higher risks. Suppose the adverse effects of these risks are not immediately apparent. In that case, the market can evolve in a way that blinds consumers to these dangers, explained Michelle Barnhart, an associate professor at Oregon State University’s College of Business and co-author of a recent study.

Barnhart emphasised, “It’s not solely the fault of consumers, producers, or regulators individually. It’s the combined effect of these factors that create this dilemma.” Understanding the development of such situations could assist consumers, regulators, and producers in making more informed decisions when faced with similar circumstances.

The findings of the research, published in the Journal of Consumer Research, were led by Lena Pellandini-Simanyi from the University of Lugano in Switzerland. Barnhart, who specialises in consumer culture and market systems, and Pellandini-Simanyi, a sociologist with expertise in consumer markets, focused on analysing the Hungarian mortgage crisis as a case study. They aimed to understand how risk-averse individuals opted for high-risk products or services.

To delve into the consumer mindset, the researchers conducted 47 interviews with Hungarian borrowers who had taken out mortgages in local or higher-risk foreign currencies between 2001 and 2010. They also surveyed a broader group of mortgage borrowers, interviewed 37 finance and mortgage industry experts and regulators, and analysed regulatory documents and parliamentary proceedings.

Their investigation uncovered patterns that demonstrated mortgages becoming progressively riskier over time and social and market changes that led consumers to overlook these escalating risks collectively. Moreover, they identified specific characteristics that facilitated these patterns, suggesting that similar markets might evolve in comparable ways.

“In typical scenarios involving new products, early adopters tend to be quite skeptical. They scrutinise the product extensively, become well-informed about its nuances, and assess its risks comprehensively,” explained Pellandini-Sumanyi. “However, if these early adopters experience success with the product, subsequent consumers may assume it will perform similarly for them, often without conducting the same level of scrutiny, even as product quality may decline.”

Barnhart added, “This sets off a chain reaction where quality diminishes due to the rush to meet consumer demand and maintain profitability. Meanwhile, consumers increasingly rely on social signals indicating the product is safe, without critically assessing how its risks may have changed.”

The researchers also identified a ‘prudence paradox’ in which the most risk-averse individuals delay market entry only to purchase the riskiest products because they wait too long. This cycle typically only breaks with external intervention, such as market corrections or regulatory measures. For instance, while cosmetic surgery is generally safe, an influx of low-cost procedures at inadequately equipped facilities led to a surge in botched surgeries until regulatory oversight caught up.

“These findings underscore the influence of social information,” Barnhart noted. “In such environments, individual consumers find it exceedingly challenging to assess risks independently, as this deviates significantly from established norms.”

To shield themselves against collective ignorance, consumers are advised to meticulously evaluate personal risk factors about others facing similar circumstances, as suggested by Pellandini-Sumanyi. “Ensure your comparisons are apples-to-apples in terms of products and consumer situations,” she concluded.

More information: Léna Pellandini-Simányi et al, The Market Dynamics of Collective Ignorance and Spiraling Risk, Journal of Consumer Research. DOI: 10.1093/jcr/ucae018

Journal information: Journal of Consumer Research Provided by Oregon State University